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Dissertations / Theses on the topic 'Segmentation; Feature tracking; Computer vision'
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Wiles, Charles S. "Closing the loop on multiple motions." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320152.
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Graves, Alex. "GPU-Accelerated Feature Tracking." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1462372516.
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Möller, Sebastian. "Image Segmentation and Target Tracking using Computer Vision." Thesis, Linköpings universitet, Datorseende, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-68061.
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In this master thesis the possibility of detecting and tracking objects in multispectral infrared video sequences is investigated. The current method with fix-sized rectangles have significant disadvantages. These disadvantages will be solved using image segmentation to estimate the shape of the object. The result of the image segmentation is used to determine the infrared contrast of the object. Our results show how some objects will give very good segmentation, tracking as well as shape detection. The objects that perform best are the flares and countermeasures. But especially helicopters seen from the side, with significant movements, is better detected with our method. The motion of the object is very important since movement is the main component in successful shape detection. This is so because helicopters are much colder than flares and engines. Detecting the presence and position of moving objects is easier and can be done quite successfully even with helicopters. But using structure tensors we can also detect the presence and estimate the position for stationary objects.I detta examensarbete undersöks möjligheterna att detektera och spåra intressanta objekt i multispektrala infraröda videosekvenser. Den nuvarande metoden, som använder sig av rektanglar med fix storlek, har sina nackdelar. Dessa nackdelar kommer att lösas med hjälp av bildsegmentering för att uppskatta formen på önskade mål.Utöver detektering och spårning försöker vi också att hitta formen och konturen för intressanta objekt för att kunna använda den exaktare passformen vid kontrastberäkningar. Denna framsegmenterade kontur ersätter de gamla fixa rektanglarna som använts tidigare för att beräkna intensitetskontrasten för objekt i de infraröda våglängderna. Resultaten som presenteras visar att det för vissa objekt, som motmedel och facklor, är lättare att få fram en bra kontur samt målföljning än vad det är med helikoptrar, som var en annan önskad måltyp. De svårigheter som uppkommer med helikoptrar beror till stor del på att de är mycket svalare vilket gör att delar av helikoptern kan helt döljas i bruset från bildsensorn. För att kompensera för detta används metoder som utgår ifrån att objektet rör sig mycket i videon så att rörelsen kan användas som detekteringsparameter. Detta ger bra resultat för de videosekvenser där målet rör sig mycket i förhållande till sin storlek.
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Rowe, Simon Michael. "Robust feature search for active tracking." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318616.
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Pretorius, Eugene. "An adaptive feature-based tracking system." Thesis, Link to the online version, 2008. http://hdl.handle.net/10019/1441.
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Lan, Xiangyuan. "Multi-cue visual tracking: feature learning and fusion." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/319.
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As an important and active research topic in computer vision community, visual tracking is a key component in many applications ranging from video surveillance and robotics to human computer. In this thesis, we propose new appearance models based on multiple visual cues and address several research issues in feature learning and fusion for visual tracking. Feature extraction and feature fusion are two key modules to construct the appearance model for the tracked target with multiple visual cues. Feature extraction aims to extract informative features for visual representation of the tracked target, and many kinds of hand-crafted feature descriptors which capture different types of visual information have been developed. However, since large appearance variations, e.g. occlusion, illumination may occur during tracking, the target samples may be contaminated/corrupted. As such, the extracted raw features may not be able to capture the intrinsic properties of the target appearance. Besides, without explicitly imposing the discriminability, the extracted features may potentially suffer background distraction problem. To extract uncontaminated discriminative features from multiple visual cues, this thesis proposes a novel robust joint discriminative feature learning framework which is capable of 1) simultaneously and optimally removing corrupted features and learning reliable classifiers, and 2) exploiting the consistent and feature-specific discriminative information of multiple feature. In this way, the features and classifiers learned from potentially corrupted tracking samples can be better utilized for target representation and foreground/background discrimination. As shown by the Data Processing Inequality, information fusion in feature level contains more information than that in classifier level. In addition, not all visual cues/features are reliable, and thereby combining all the features may not achieve a better tracking performance. As such, it is more reasonable to dynamically select and fuse multiple visual cues for visual tracking. Based on aforementioned considerations, this thesis proposes a novel joint sparse representation model in which feature selection, fusion, and representation are performed optimally in a unified framework. By taking advantages of sparse representation, unreliable features are detected and removed while reliable features are fused on feature level for target representation. In order to capture the non-linear similarity of features, the model is further extended to perform feature fusion in kernel space. Experimental results demonstrate the effectiveness of the proposed model. Since different visual cues extracted from the same object should share some commonalities in their representations and each feature should also have some diversities to reflect its complementarity in appearance modeling, another important problem in feature fusion is how to learn the commonality and diversity in the fused representations of multiple visual cues to enhance the tracking accuracy. Different from existing multi-cue sparse trackers which only consider the commonalities among the sparsity patterns of multiple visual cues, this thesis proposes a novel multiple sparse representation model for multi-cue visual tracking which jointly exploits the underlying commonalities and diversities of different visual cues by decomposing multiple sparsity patterns. Moreover, this thesis introduces a novel online multiple metric learning to efficiently and adaptively incorporate the appearance proximity constraint, which ensures that the learned commonalities of multiple visual cues are more representative. Experimental results on tracking benchmark videos and other challenging videos show that the proposed tracker achieves better performance than the existing sparsity-based trackers and other state-of-the-art trackers.
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Sun, Shijun. "Video object segmentation and tracking using VSnakes /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/6038.
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Roberts, Jonathan Michael. "Attentive visual tracking and trajectory estimation for dynamic scene segmentation." Thesis, University of Southampton, 1994. https://eprints.soton.ac.uk/250163/.
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Intelligent Co-Pilot Systems (ICPS) offer the next challenge to vehicle-highway automation. The key to ICPSs is the detection of moving objects (other vehicles) from the moving observer using a visual sensor. The aim of the work presented in this thesis was to design and implement a feature detection and tracking strategy that is capable of tracking image features independently, in parallel, and in real-time and to cluster/segment features utilising the inherent temporal information contained within feature trajectories. Most images contain areas that are of little or no interest to vision tasks. An attentive, data-driven, approach to feature detection and tracking is proposed which aims to increase the efficiency of feature detection and tracking by focusing attention onto relevant regions of the image likely to contain scene structure. This attentive algorithm lends itself naturally to parallelisation and results from a parallel implementation are presented. A scene may be segmented into independently moving objects based on the assumption that features belonging to the same object will move in an identical way in three dimensions (this assumes objects are rigid). A model for scene segmentation is proposed that uses information contained within feature trajectories to cluster, or group, features into independently moving objects. This information includes: image-plane position, time-to-collision of a feature with the image-plane, and the type of motion observed. The Multiple Model Adaptive Estimator (MMAE) algorithm is extended to cope with constituent filters with different states (MMAE2) in an attempt to accurately estimate the time-to-collision of a feature and provide a reliable idea of the type of motion observed (in the form of a model belief measure). Finally, poor state initialisation is identified as a likely prime cause for poor Extended Kalman Filter (EKF) performance (and hence poor MMAE2 performance) when using high order models. The idea of the neurofuzzy initialised EKF (NF-EKF) is introduced which attempts to reduce the time for an EKF to converge by improving the accuracy of the EKF's initial state estimates.
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Roychoudhury, Shoumik. "Tracking Human in Thermal Vision using Multi-feature Histogram." Master's thesis, Temple University Libraries, 2012. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/203794.
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Electrical EngineeringM.S.E.E.
This thesis presents a multi-feature histogram approach to track a person in thermal vision. Illumination variation is a primary constraint in the performance of object tracking in visible spectrum. Thermal infrared (IR) sensor, which measures the heat energy emitted from an object, is less sensitive to illumination variations. Therefore, thermal vision has immense advantage in object tracking in varying illumination conditions. Kernel based approaches such as mean shift tracking algorithm which uses a single feature histogram for object representation, has gained popularity in the field of computer vision due its efficiency and robustness to track non-rigid object in significant complex background. However, due to low resolution of IR images the gray level intensity information is not sufficient enough to give a strong cue for object representation using histogram. Multi-feature histogram, which is the combination of the gray level intensity information and edge information, generates an object representation which is more robust in thermal vision. The objective of this research is to develop a robust human tracking system which can autonomously detect, identify and track a person in a complex thermal IR scene. In this thesis the tracking procedure has been adapted from the well-known and efficient mean shift tracking algorithm and has been modified to enable fusion of multiple features to increase the robustness of the tracking procedure in thermal vision. In order to identify the object of interest before tracking, rapid human detection in thermal IR scene is achieved using Adaboost classification algorithm. Furthermore, a computationally efficient body pose recognition method is developed which uses Hu-invariant moments for matching object shapes. An experimental setup consisting of a Forward Looking Infrared (FLIR) camera, mounted on a Pioneer P3-DX mobile robot platform was used to test the proposed human tracking system in both indoor and uncontrolled outdoor environments. The performance evaluation of the proposed tracking system on the OTCBVS benchmark dataset shows improvement in tracking performance in comparison to the traditional mean-shift tracking algorithm. Moreover, experimental results in different indoor and outdoor tracking scenarios involving different appearances of people show tracking is robust under cluttered background, varying illumination and partial occlusion of target object.
Temple University--Theses
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Fang, Jian. "Optical Imaging and Computer Vision Technology for Corn Quality Measurement." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/733.
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The official U.S. standards for corn have been available for almost one hundred years. Corn grading system has been gradually updated over the years. In this thesis, we investigated a fast corn grading system, which includes the mechanical part and the computer recognition part. The mechanical system can deliver the corn kernels onto the display plate. For the computer recognition algorithms, we extracted common features from each corn kernel, and classified them to measure the grain quality.
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Jackson, Jeremy D. "Layered Deformotion with Radiance: A Model for Appearance, Segmentation, Registration, and Tracking." Diss., Available online, Georgia Institute of Technology, 2007, 2007. http://etd.gatech.edu/theses/available/etd-07092007-104249/.
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Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008.Vela, Patricio, Committee Member ; Tannenbaum, Allen, Committee Member ; Yezzi, Anthony, Committee Chair ; Turk, Greg, Committee Member ; Lanterman, Aaron, Committee Member.
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Ozertem, Kemal Arda. "Vision-assisted Object Tracking." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614073/index.pdf.
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In this thesis, a video tracking method is proposed that is based on both computer vision and estimation theory. For this purpose, the overall study is partitioned into four related subproblems. The first part is moving object detectionfor moving object detection, two different background modeling methods are developed. The second part is feature extraction and estimation of optical flow between video frames. As the feature extraction method, a well-known corner detector algorithm is employed and this extraction is applied only at the moving regions in the scene. For the feature points, the optical flow vectors are calculated by using an improved version of Kanade Lucas Tracker. The resulting optical flow field between consecutive frames is used directly in proposed tracking method. In the third part, a particle filter structure is build to provide tracking process. However, the particle filter is improved by adding optical flow data to the state equation as a correction term. In the last part of the study, the performance of the proposed approach is compared against standard implementations particle filter based trackers. Based on the simulation results in this study, it could be argued that insertion of vision-based optical flow estimation to tracking formulation improves the overall performance.
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Arif, Omar. "Robust target localization and segmentation using statistical methods." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33882.
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This thesis aims to contribute to the area of visual tracking, which is the process of identifying an object of interest through a sequence of successive images. The thesis explores kernel-based statistical methods, which map the data to a higher dimensional space. A pre-image framework is provided to find the mapping from the embedding space to the input space for several manifold learning and dimensional learning algorithms. Two algorithms are developed for visual tracking that are robust to noise and occlusions. In the first algorithm, a kernel PCA-based eigenspace representation is used. The de-noising and clustering capabilities of the kernel PCA procedure lead to a robust algorithm. This framework is extended to incorporate the background information in an energy based formulation, which is minimized using graph cut and to track multiple objects using a single learned model. In the second method, a robust density comparison framework is developed that is applied to visual tracking, where an object is tracked by minimizing the distance between a model distribution and given candidate distributions. The superior performance of kernel-based algorithms comes at a price of increased storage and computational requirements. A novel method is developed that takes advantage of the universal approximation capabilities of generalized radial basis function neural networks to reduce the computational and storage requirements for kernel-based methods.
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Krieger, Evan. "Adaptive Fusion Approach for Multiple Feature Object Tracking." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton15435905735447.
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Hou, Yali, and 侯亚丽. "Video-based people counting and crowd segmentation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B47032339.
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Dambreville, Samuel. "Statistical and geometric methods for shape-driven segmentation and tracking." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/22707.
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Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008.Committee Chair: Allen Tannenbaum; Committee Member: Anthony Yezzi; Committee Member: Marc Niethammer; Committee Member: Patricio Vela; Committee Member: Yucel Altunbasak.
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Pal, Chris. "A Probabilistic Approach to Image Feature Extraction, Segmentation and Interpretation." Thesis, University of Waterloo, 2000. http://hdl.handle.net/10012/1049.
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This thesis describes a probabilistic approach to imagesegmentation and interpretation. The focus of the investigation is the development of a systematic way of combining color, brightness, texture and geometric features extracted from an image to arrive at a consistent interpretation for each pixel in the image. The contribution of this thesis is thus the presentation of a novel framework for the fusion of extracted image features producing a segmentation of an image into relevant regions. Further, a solution to the sub-pixel mixing problem is presented based on solving a probabilistic linear program. This work is specifically aimed at interpreting and digitizing multi-spectral aerial imagery of the Earth's surface. The features of interest for extraction are those of relevance to environmental management, monitoring and protection. The presented algorithms are suitable for use within a larger interpretive system. Some results are presented and contrasted with other techniques. The integration of these algorithms into a larger system is based firmly on a probabilistic methodology and the use of statistical decision theory to accomplish uncertain inference within the visual formalism of a graphical probability model.
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Parks, Matthew Raymond. "Vision-Based Self-Motion Estimation in a Fixed-Wing Aerial Vehicle." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/33855.
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This paper describes a complete algorithm to estimate the motion of a fixed-wing aircraft given a series of digitized flight images. The algorithm was designed for fixed-wing aircraft because carefully procured flight images and corresponding navigation data were available to us for testing. After image pre-processing, optic flow data is determined by automatically finding and tracking good features between pairs of images. The image coordinates of matched features are then processed by a rigid-object linear optic flow-motion estimation algorithm. Input factors are weighed to provide good testing techniques. Error analysis is performed with simulation data keeping these factors in mind to determine the effectiveness of the optic flow algorithm. The output of this program is an estimate of rotation and translation of the imaged environment in relation to the camera, and thereby the airplane. Real flight images from NASA test flights are used to confirm the accuracy of the algorithm. Where possible, the estimated motion parameters are compared with recorded flight instrument data to confirm the correctness of the algorithm. Results show that the algorithm is accurate to within a degree provided that enough optic flow feature points are tracked.
Master of Science
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Tran, Antoine. "Object representation in local feature spaces : application to real-time tracking and detection." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLY010/document.
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La représentation visuelle est un problème fondamental en vision par ordinateur. Le but est de réduire l'information au strict nécessaire pour une tâche désirée. Plusieurs types de représentation existent, comme les caractéristiques de couleur (histogrammes, attributs de couleurs...), de forme (dérivées, points d'intérêt...) ou d'autres, comme les bancs de filtres.Les caractéristiques bas-niveau (locales) sont rapides à calculer. Elles ont un pouvoir de représentation limité, mais leur généricité présente un intérêt pour des systèmes autonomes et multi-tâches, puisque les caractéristiques haut-niveau découlent d'elles.Le but de cette thèse est de construire puis d'étudier l'impact de représentations fondées seulement sur des caractéristiques locales de bas-niveau (couleurs, dérivées spatiales) pour deux tâches : la poursuite d'objets génériques, nécessitant des caractéristiques robustes aux variations d'aspect de l'objet et du contexte au cours du temps; la détection d'objets, où la représentation doit décrire une classe d'objets en tenant compte des variations intra-classe. Plutôt que de construire des descripteurs d'objets globaux dédiés, nous nous appuyons entièrement sur les caractéristiques locales et sur des mécanismes statistiques flexibles visant à estimer leur distribution (histogrammes) et leurs co-occurrences (Transformée de Hough Généralisée). La Transformée de Hough Généralisée (THG), créée pour la détection de formes quelconques, consiste à créer une structure de données représentant un objet, une classe... Cette structure, d'abord indexée par l'orientation du gradient, a été étendue à d'autres caractéristiques. Travaillant sur des caractéristiques locales, nous voulons rester proche de la THG originale.En poursuite d'objets, après avoir présenté nos premiers travaux, combinant la THG avec un filtre particulaire (utilisant un histogramme de couleurs), nous présentons un algorithme plus léger et rapide (100fps), plus précis et robuste. Nous présentons une évaluation qualitative et étudierons l'impact des caractéristiques utilisées (espace de couleur, formulation des dérivées partielles...). En détection, nous avons utilisé l'algorithme de Gall appelé forêts de Hough. Notre but est de réduire l'espace de caractéristiques utilisé par Gall, en supprimant celles de type HOG, pour ne garder que les dérivées partielles et les caractéristiques de couleur. Pour compenser cette réduction, nous avons amélioré deux étapes de l'entraînement : le support des descripteurs locaux (patchs) est partiellement produit selon une mesure géométrique, et l'entraînement des nœuds se fait en générant une carte de probabilité spécifique prenant en compte les patchs utilisés pour cette étape. Avec l'espace de caractéristiques réduit, le détecteur n'est pas plus précis. Avec les mêmes caractéristiques que Gall, sur une même durée d'entraînement, nos travaux ont permis d'avoir des résultats identiques, mais avec une variance plus faible et donc une meilleure répétabilitéVisual representation is a fundamental problem in computer vision. The aim is to reduce the information to the strict necessary for a query task. Many types of representation exist, like color features (histograms, color attributes...), shape ones (derivatives, keypoints...) or filterbanks.Low-level (and local) features are fast to compute. Their power of representation are limited, but their genericity have an interest for autonomous or multi-task systems, as higher level ones derivate from them. We aim to build, then study impact of low-level and local feature spaces (color and derivatives only) for two tasks: generic object tracking, requiring features robust to object and environment's aspect changes over the time; object detection, for which the representation should describe object class and cope with intra-class variations.Then, rather than using global object descriptors, we use entirely local features and statisticals mecanisms to estimate their distribution (histograms) and their co-occurrences (Generalized Hough Transform).The Generalized Hough Transform (GHT), created for detection of any shape, consists in building a codebook, originally indexed by gradient orientation, then to diverse features, modeling an object, a class. As we work on local features, we aim to remain close to the original GHT.In tracking, after presenting preliminary works combining the GHT with a particle filter (using color histograms), we present a lighter and fast (100 fps) tracker, more accurate and robust.We present a qualitative evaluation and study the impact of used features (color space, spatial derivative formulation).In detection, we used Gall's Hough Forest. We aim to reduce Gall's feature space and discard HOG features, to keep only derivatives and color ones.To compensate the reduction, we enhanced two steps: the support of local descriptors (patches) are partially chosen using a geometrical measure, and node training is done by using a specific probability map based on patches used at this step.With reduced feature space, the detector is less accurate than with Gall's feature space, but for the same training time, our works lead to identical results, but with higher stability and then better repeatability
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Magnusson, Klas E. G. "Segmentation and tracking of cells and particles in time-lapse microscopy." Doctoral thesis, KTH, Signalbehandling, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-196911.
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In biology, many different kinds of microscopy are used to study cells. There are many different kinds of transmission microscopy, where light is passed through the cells, that can be used without staining or other treatments that can harm the cells. There is also fluorescence microscopy, where fluorescent proteins or dyes are placed in the cells or in parts of the cells, so that they emit light of a specific wavelength when they are illuminated with light of a different wavelength. Many fluorescence microscopes can take images on many different depths in a sample and thereby build a three-dimensional image of the sample. Fluorescence microscopy can also be used to study particles, for example viruses, inside cells. Modern microscopes often have digital cameras or other equipment to take images or record time-lapse video. When biologists perform experiments on cells, they often record image sequences or sequences of three-dimensional volumes to see how the cells behave when they are subjected to different drugs, culture substrates, or other external factors. Previously, the analysis of recorded data has often been done manually, but that is very time-consuming and the results often become subjective and hard to reproduce. Therefore there is a great need for technology for automated analysis of image sequences with cells and particles inside cells. Such technology is needed especially in biological research and drug development. But the technology could also be used clinically, for example to tailor a cancer treatment to an individual patient by evaluating different treatments on cells from a biopsy. This thesis presents algorithms to find cells and particles in images, and to calculate tracks that show how they have moved during an experiment. We have developed a complete system that can find and track cells in all commonly used imaging modalities. We selected and extended a number of existing segmentation algorithms, and thereby created a complete tool to find cell outlines. To link the segmented objects into tracks, we developed a new track linking algorithm. The algorithm adds tracks one by one using dynamic programming, and has many advantages over prior algorithms. Among other things, it is fast, it calculates tracks which are optimal for the entire image sequence, and it can handle situations where multiple cells have been segmented incorrectly as one object. To make it possible to use information about the velocities of the objects in the linking, we developed a method where the positions of the objects are preprocessed using a filter before the linking is performed. This is important for tracking of some particles inside cells and for tracking of cell nuclei in some embryos. We have developed an open source software which contains all tools that are necessary to analyze image sequences with cells or particles. It has tools for segmentation and tracking of objects, optimization of settings, manual correction, and analysis of outlines and tracks. We developed the software together with biologists who used it in their research. The software has already been used for data analysis in a number of biology publications. Our system has also achieved outstanding performance in three international objective comparisons of systems for tracking of cells.Inom biologi används många olika typer av mikroskopi för att studera celler. Det finns många typer av genomlysningsmikroskopi, där ljus passerar genom cellerna, som kan användas utan färgning eller andra åtgärder som riskerar att skada cellerna. Det finns också fluorescensmikroskopi där fluorescerande proteiner eller färger förs in i cellerna eller i delar av cellerna, så att de emitterar ljus av en viss våglängd då de belyses med ljus av en annan våglängd. Många fluorescensmikroskop kan ta bilder på flera olika djup i ett prov och på så sätt bygga upp en tre-dimensionell bild av provet. Fluorescensmikroskopi kan även användas för att studera partiklar, som exempelvis virus, inuti celler. Moderna mikroskop har ofta digitala kameror eller liknande utrustning för att ta bilder och spela in bildsekvenser. När biologer gör experiment på celler spelar de ofta in bildsekvenser eller sekvenser av tre-dimensionella volymer för att se hur cellerna beter sig när de utsätts för olika läkemedel, odlingssubstrat, eller andra yttre faktorer. Tidigare har analysen av inspelad data ofta gjorts manuellt, men detta är mycket tidskrävande och resultaten blir ofta subjektiva och svåra att reproducera. Därför finns det ett stort behov av teknik för automatiserad analys av bildsekvenser med celler och partiklar inuti celler. Sådan teknik behövs framförallt inom biologisk forskning och utveckling av läkemedel. Men tekniken skulle också kunna användas kliniskt, exempelvis för att skräddarsy en cancerbehandling till en enskild patient genom att utvärdera olika behandlingar på celler från en biopsi. I denna avhandling presenteras algoritmer för att hitta celler och partiklar i bilder, och för att beräkna trajektorier som visar hur de har förflyttat sig under ett experiment. Vi har utvecklat ett komplett system som kan hitta och följa celler i alla vanligt förekommande typer av mikroskopi. Vi valde ut och vidareutvecklade ett antal existerande segmenteringsalgoritmer, och skapade på så sätt ett heltäckande verktyg för att hitta cellkonturer. För att länka ihop de segmenterade objekten till trajektorier utvecklade vi en ny länkningsalgoritm. Algoritmen lägger till trajektorier en och en med hjälp av dynamisk programmering, och har många fördelar jämfört med tidigare algoritmer. Bland annat är den snabb, den beräknar trajektorier som är optimala över hela bildsekvensen, och den kan hantera fall då flera celler felaktigt segmenterats som ett objekt. För att kunna använda information om objektens hastighet vid länkningen utvecklade vi en metod där objektens positioner förbehandlas med hjälp av ett filter innan länkningen utförs. Detta är betydelsefullt för följning av vissa partiklar inuti celler och för följning av cellkärnor i vissa embryon. Vi har utvecklat en mjukvara med öppen källkod, som innehåller alla verktyg som krävs för att analysera bildsekvenser med celler eller partiklar. Den har verktyg för segmentering och följning av objekt, optimering av inställningar, manuell korrektion, och analys av konturer och trajektorier. Vi utvecklade mjukvaran i samarbete med biologer som använde den i sin forskning. Mjukvaran har redan använts för dataanalys i ett antal biologiska publikationer. Vårt system har även uppnått enastående resultat i tre internationella objektiva jämförelser av system för följning av celler.
QC 20161125
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Lankton, Shawn M. "Localized statistical models in computer vision." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31644.
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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.Committee Chair: Tannenbaum, Allen; Committee Member: Al Regib, Ghassan; Committee Member: Niethammer, Marc; Committee Member: Shamma, Jeff; Committee Member: Stillman, Arthur; Committee Member: Yezzi, Anthony. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Doyle, Jason Emory. "Automatic Dynamic Tracking of Horse Head Facial Features in Video Using Image Processing Techniques." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/87582.
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The wellbeing of horses is very important to their care takers, trainers, veterinarians, and owners. This thesis describes the development of a non-invasive image processing technique that allows for automatic detection and tracking of horse head and ear motion, respectively, in videos or camera feed, both of which may provide indications of horse pain, stress, or well-being. The algorithm developed here can automatically detect and track head motion and ear motion, respectively, in videos of a standing horse. Results demonstrating the technique for nine different horses are presented, where the data from the algorithm is utilized to plot absolute motion vs. time, velocity vs. time, and acceleration vs. time for the head and ear motion, respectively, of a variety of horses and ponies. Two-dimensional plotting of x and y motion over time is also presented. Additionally, results of pilot work in eye detection in light colored horses is also presented. Detection of pain in horses is particularly difficult because they are prey animals and have mechanisms to disguise their pain, and these instincts may be particularly strong in the presence of an unknown human, such as a veterinarian. Current state-of-the art for detecting pain in horses primarily involves invasive methods, such as heart rate monitors around the body, drawing blood for cortisol levels, and pressing on painful areas to elicit a response, although some work has been done for humans to sort and score photographs subjectively in terms of a "horse grimace scale." The algorithms developed in this thesis are the first that the author is aware for exploiting proven image processing approaches from other applications for development of an automatic tool for detection and tracking of horse facial indicators. The algorithms were done in common open source programs Python and OpenCV, and standard image processing approaches including Canny Edge detection Hue, Saturation, Value color filtering, and contour tracking were utilized in algorithm development. The work in this thesis provides the foundational development of a non -invasive and automatic detection and tracking program for horse head and ear motion, including demonstration of the viability of this approach using videos of standing horses. This approach lays the groundwork for robust tool development for monitoring horses non-invasively and without the required presence of humans in such applications as post-operative monitoring, foaling, evaluation of performance horses in competition and/or training, as well as for providing data for research on animal welfare, among other scenarios.MS
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Solis, Montero Andres. "Efficient Feature Extraction for Shape Analysis, Object Detection and Tracking." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34830.
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During the course of this thesis, two scenarios are considered. In the first one, we contribute to feature extraction algorithms. In the second one, we use features to improve object detection solutions and localization. The two scenarios give rise to into four thesis sub-goals. First, we present a new shape skeleton pruning algorithm based on contour approximation and the integer medial axis. The algorithm effectively removes unwanted branches, conserves the connectivity of the skeleton and respects the topological properties of the shape. The algorithm is robust to significant boundary noise and to rigid shape transformations. It is fast and easy to implement. While shape-based solutions via boundary and skeleton analysis are viable solutions to object detection, keypoint features are important for textured object detection. Therefore, we present a keypoint featurebased planar object detection framework for vision-based localization. We demonstrate that our framework is robust against illumination changes, perspective distortion, motion
blur, and occlusions. We increase robustness of the localization scheme in cluttered environments and decrease false detection of targets. We present an off-line target evaluation strategy and a scheme to improve pose. Third, we extend planar object detection to a real-time approach for 3D object detection using a mobile and uncalibrated camera. We develop our algorithm based on two novel naive Bayes classifiers for viewpoint and feature matching that improve performance and decrease memory usage. Our algorithm exploits the specific structure of various binary descriptors in order to boost feature matching by conserving descriptor properties. Our novel naive classifiers require a database with a small memory footprint because we only store efficiently encoded features. We improve the feature-indexing scheme to speed up the matching process creating a highly efficient database for objects. Finally, we present a model-free long-term tracking algorithm based on the Kernelized Correlation Filter. The proposed solution improves the correlation tracker based on precision, success, accuracy and robustness while increasing frame rates. We integrate adjustable Gaussian window and sparse features for robust scale estimation creating a better separation of the target and the background. Furthermore, we include fast descriptors and Fourier spectrum packed format to boost performance while decreasing the memory footprint. We compare our algorithm with state-of-the-art techniques to validate the results.
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Hannuksela, J. (Jari). "Camera based motion estimation and recognition for human-computer interaction." Doctoral thesis, University of Oulu, 2008. http://urn.fi/urn:isbn:9789514289781.
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Abstract
Communicating with mobile devices has become an unavoidable part of our daily life. Unfortunately, the current user interface designs are mostly taken directly from desktop computers. This has resulted in devices that are sometimes hard to use. Since more processing power and new sensing technologies are already available, there is a possibility to develop systems to communicate through different modalities. This thesis proposes some novel computer vision approaches, including head tracking, object motion analysis and device ego-motion estimation, to allow efficient interaction with mobile devices.
For head tracking, two new methods have been developed. The first method detects a face region and facial features by employing skin detection, morphology, and a geometrical face model. The second method, designed especially for mobile use, detects the face and eyes using local texture features. In both cases, Kalman filtering is applied to estimate the 3-D pose of the head. Experiments indicate that the methods introduced can be applied on platforms with limited computational resources.
A novel object tracking method is also presented. The idea is to combine Kalman filtering and EM-algorithms to track an object, such as a finger, using motion features. This technique is also applicable when some conventional methods such as colour segmentation and background subtraction cannot be used. In addition, a new feature based camera ego-motion estimation framework is proposed. The method introduced exploits gradient measures for feature selection and feature displacement uncertainty analysis. Experiments with a fixed point implementation testify to the effectiveness of the approach on a camera-equipped mobile phone.
The feasibility of the methods developed is demonstrated in three new mobile interface solutions. One of them estimates the ego-motion of the device with respect to the user's face and utilises that information for browsing large documents or bitmaps on small displays. The second solution is to use device or finger motion to recognize simple gestures. In addition to these applications, a novel interactive system to build document panorama images is presented.
The motion estimation and recognition techniques presented in this thesis have clear potential to become practical means for interacting with mobile devices. In fact, cameras in future mobile devices may, for the most of time, be used as sensors for self intuitive user interfaces rather than using them for digital photography.
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Flodin, Frida. "Improved Data Association for Multi-Pedestrian Tracking Using Image Information." Thesis, Linköpings universitet, Datorseende, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-169419.
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Multi-pedestrian tracking (MPT) is the task of localizing and following the trajectory of pedestrians in a sequence. Using an MPT algorithm is an important part in preventing pedestrian-vehicle collisions in Automated Driving (AD) and Advanced Driving Assistance Systems (ADAS). It has benefited greatly from the advances in computer vision and machine learning in the last decades. Using a pedestrian detector, the tracking consists of associating the detections between frames and maintaining pedestrian identities throughout the sequence. This can be a challenging task due to occlusions, missed detections and complex scenes. The number of pedestrians is unknown, and it varies with time. Finding new methods for improving MPT is an active research field and there are many approaches found in the literature. This work focuses on improving the detection-to-track association, the data association, with the help of extracted color features for each pedestrian. Utilizing the recent improvements in object detection this work shows that classical color features still is relevant in pedestrian tracking for real time applications with limited computational resources. The appearance is not only used in the data association but also integrated in a new proposed method to avoid tracking errors due to missed detections. The results show that even with simple models the color appearance can be used to improve the tracking results. Evaluation on the commonly used Multi-Object Tracking-benchmark shows an improvement in the Multi-Object Tracking Accuracy and identity switches, while keeping other measures essentially unchanged.
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Desai, Alok. "An Efficient Feature Descriptor and Its Real-Time Applications." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5465.
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Finding salient features in an image, and matching them to their corresponding features in another image is an important step for many vision-based applications. Feature description plays an important role in the feature matching process. A robust feature descriptor must works with a number of image deformations and should be computationally efficient. For resource-limited systems, floating point and complex operations such as multiplication and square root are not desirable. This research first introduces a robust and efficient feature descriptor called PRObability (PRO) descriptor that meets these requirements without sacrificing matching accuracy. The PRO descriptor is further improved by incorporating only affine features for matching. While performing well, PRO descriptor still requires larger descriptor size, higher offline computation time, and more memory space than other binary feature descriptors. SYnthetic BAsis (SYBA) descriptor is developed to overcome these drawbacks. SYBA is built on the basis of a new compressed sensing theory that uses synthetic basis functions to uniquely encode or reconstruct a signal. The SYBA descriptor is designed to provide accurate feature matching for real-time vision applications. To demonstrate its performance, we develop algorithms that utilize SYBA descriptor to localize the soccer ball in a broadcast soccer game video, track ground objects for unmanned aerial vehicle, and perform motion analysis, and improve visual odometry accuracy for advanced driver assistance systems. SYBA provides high feature matching accuracy with computational simplicity and requires minimal computational resources. It is a hardware-friendly feature description and matching algorithm suitable for embedded vision applications.
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Lindner, Claudia. "Statistical shape analysis of the proximal femur : development of a fully automatic segmentation system and its applications." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/statistical-shape-analysis-of-the-proximal-femur-development-of-a-fully-automatic-segmentation-system-and-its-applications(b36076bd-32da-4b00-9518-d05060aaa594).html.
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Osteoarthritis (OA) is the most common form of human joint disease causing significant pain and disability. Current treatment for hip OA is limited to pain management and joint replacement for end-stage disease. The development of methods for early diagnosis and new treatment options are urgently needed to minimise the impact of the disease. Studies of hip OA have shown that hip joint morphology correlates with susceptibility to hip OA and disease progression. Bone shape analyses play an important role in disease diagnosis, pre-operative planning, and treatment analysis as well as in epidemiological studies aimed at identifying risk factors for hip OA. Statistical Shape Models (SSMs) are being increasingly applied to imaging-based bone shape analyses as they provide a means of quantitatively describing the global shape of the bone. This is in contrast to conventional clinical and research practice where the analysis of bone shape is reduced to a series of measurements of lengths and angles. This thesis describes the development of a novel fully automatic software system that segments the proximal femur from anteroposterior (AP) pelvic radiographs by densely placing 65 points along its contour. These annotations can then be used for the detailed morphometric analysis of proximal femur shape. The performance of the system was evaluated on a large dataset of 839 radiographs of mixed quality. Achieving a mean point-to-curve error of less than 0.9mm for 99% of all 839 AP pelvic radiographs, this is the most accurate and robust automatic method for segmenting the proximal femur in two-dimensional radiographs yet published. The system was also applied to a number of morphometric analyses of the proximal femur, showing that SSM-based radiographic proximal femur shape significantly differs between males and females, and is highly symmetric between the left and right hip joint of an individual. In addition, the research described in this thesis demonstrates how the point annotations resulting from the system can be used for univariate and multivariate genetic association analyses, identifying three novel genetic variants that contribute to radiographic proximal femur shape while also showing an association with hip OA.The developed system will facilitate complex morphometric and genetic analyses of shape variation of the proximal femur across large datasets, paving the way for the development of new options to diagnose, treat and prevent hip OA.
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Ärleryd, Sebastian. "Realtime Virtual 3D Image of Kidney Using Pre-Operative CT Image for Geometry and Realtime US-Image for Tracking." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-234991.
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In this thesis a method is presented to provide a 3D visualization of the human kidney and surrounding tissue during kidney surgery. The method takes advantage of the high detail of 3D X-Ray Computed Tomography (CT) and the high time resolution of Ultrasonography (US). By extracting the geometry from a single preoperative CT scan and animating the kidney by tracking its position in real time US images, a 3D visualization of the surgical volume can be created. The first part of the project consisted of building an imaging phantom as a simplified model of the human body around the kidney. It consists of three parts: the shell part representing surrounding tissue, the kidney part representing the kidney soft tissue and a kidney stone part embedded in the kidney part. The shell and soft tissue kidney parts was cast with a mixture of the synthetic polymer Polyvinyl Alchohol (PVA) and water. The kidney stone part was cast with epoxy glue. All three parts where designed to look like human tissue in CT and US images. The method is a pipeline of stages that starts with acquiring the CT image as a 3D matrix of intensity values. This matrix is then segmented, resulting in separate polygonal 3D models for the three phantom parts. A scan of the model is then performed using US, producing a sequence of US images. A computer program extracts easily recognizable image feature points from the images in the sequence. Knowing the spatial position and orientation of a new US image in which these features can be found again allows the position of the kidney to be calculated. The presented method is realized as a proof of concept implementation of the pipeline. The implementation displays an interactive visualization where the kidney is positioned according to a user-selected US image scanned for image features. Using the proof of concept implementation as a guide, the accuracy of the proposed method is estimated to be bounded by the acquired image data. For high resolution CT and US images, the accuracy can be in the order of a few millimeters.
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Lind, Johan. "Make it Meaningful : Semantic Segmentation of Three-Dimensional Urban Scene Models." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143599.
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Semantic segmentation of a scene aims to give meaning to the scene by dividing it into meaningful — semantic — parts. Understanding the scene is of great interest for all kinds of autonomous systems, but manual annotation is simply too time consuming, which is why there is a need for an alternative approach. This thesis investigates the possibility of automatically segmenting 3D-models of urban scenes, such as buildings, into a predetermined set of labels. The approach was to first acquire ground truth data by manually annotating five 3D-models of different urban scenes. The next step was to extract features from the 3D-models and evaluate which ones constitutes a suitable feature space. Finally, three supervised learners were implemented and evaluated: k-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Classification Forest (RCF). The classifications were done point-wise, classifying each 3D-point in the dense point cloud belonging to the model being classified. The result showed that the best suitable feature space is not necessarily the one containing all features. The KNN classifier got the highest average accuracy overall models — classifying 42.5% of the 3D points correct. The RCF classifier managed to classify 66.7% points correct in one of the models, but had worse performance for the rest of the models and thus resulting in a lower average accuracy compared to KNN. In general, KNN, SVM, and RCF seemed to have different benefits and drawbacks. KNN is simple and intuitive but by far the slowest classifier when dealing with a large set of training data. SVM and RCF are both fast but difficult to tune as there are more parameters to adjust. Whether the reason for obtaining the relatively low highest accuracy was due to the lack of ground truth training data, unbalanced validation models, or the capacity of the learners, was never investigated due to a limited time span. However, this ought to be investigated in future studies.
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Aranda, Joan. "Aportació als mètodes de seguiment tridimensional d'objectes d'alta velocitat d'operació mitjançant l'estereovisió." Doctoral thesis, Universitat Politècnica de Catalunya, 1997. http://hdl.handle.net/10803/6205.
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Ndiour, Ibrahima Jacques. "Dynamic curve estimation for visual tracking." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37283.
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This thesis tackles the visual tracking problem as a target contour estimation problem in the face of corrupted measurements. The major aim is to design robust recursive curve filters for accurate contour-based tracking. The state-space representation adopted comprises of a group component and a shape component describing the rigid motion and the non-rigid shape deformation respectively; filtering strategies on each component are then decoupled. The thesis considers two implicit curve descriptors, a classification probability field and the traditional signed distance function, and aims to develop an optimal probabilistic contour observer and locally optimal curve filters. For the former, introducing a novel probabilistic shape description simplifies the filtering problem on the infinite-dimensional space of closed curves to a series of point-wise filtering tasks. The definition and justification of a novel update model suited to the shape space, the derivation of the filtering equations and the relation to Kalman filtering are studied. In addition to the temporal consistency provided by the filtering, extensions involving distributed filtering methods are considered in order to maintain spatial consistency. For the latter, locally optimal closed curve filtering strategies involving curve velocities are explored. The introduction of a local, linear description for planar curve variation and curve uncertainty enables the derivation of a mechanism for estimating the optimal gain associated to the curve filtering process, given quantitative uncertainty levels. Experiments on synthetic and real sequences of images validate the filtering designs.
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Edwards, Barrett Bruce. "An Onboard Vision System for Unmanned Aerial Vehicle Guidance." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2381.
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The viability of small Unmanned Aerial Vehicles (UAVs) as a stable platform for specific application use has been significantly advanced in recent years. Initial focus of lightweight UAV development was to create a craft capable of stable and controllable flight. This is largely a solved problem. Currently, the field has progressed to the point that unmanned aircraft can be carried in a backpack, launched by hand, weigh only a few pounds and be capable of navigating through unrestricted airspace. The most basic use of a UAV is to visually observe the environment and use that information to influence decision making. Previous attempts at using visual information to control a small UAV used an off-board approach where the video stream from an onboard camera was transmitted down to a ground station for processing and decision making. These attempts achieved limited results as the two-way transmission time introduced unacceptable amounts of latency into time-sensitive control algorithms. Onboard image processing offers a low-latency solution that will avoid the negative effects of two-way communication to a ground station. The first part of this thesis will show that onboard visual processing is capable of meeting the real-time control demands of an autonomous vehicle, which will also include the evaluation of potential onboard computing platforms. FPGA-based image processing will be shown to be the ideal technology for lightweight unmanned aircraft. The second part of this thesis will focus on the exact onboard vision system implementation for two proof-of-concept applications. The first application describes the use of machine vision algorithms to locate and track a target landing site for a UAV. GPS guidance was insufficient for this task. A vision system was utilized to localize the target site during approach and provide course correction updates to the UAV. The second application describes a feature detection and tracking sub-system that can be used in higher level application algorithms.
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Molin, Joel. "Foreground Segmentation of Moving Objects." Thesis, Linköping University, Department of Electrical Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-52544.
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Foreground segmentation is a common first step in tracking and surveillance applications. The purpose of foreground segmentation is to provide later stages of image processing with an indication of where interesting data can be found. This thesis is an investigation of how foreground segmentation can be performed in two contexts: as a pre-step to trajectory tracking and as a pre-step in indoor surveillance applications.
Three methods are selected and detailed: a single Gaussian method, a Gaussian mixture model method, and a codebook method. Experiments are then performed on typical input video using the methods. It is concluded that the Gaussian mixture model produces the output which yields the best trajectories when used as input to the trajectory tracker. An extension is proposed to the Gaussian mixture model which reduces shadow, improving the performance of foreground segmentation in the surveillance context.
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Petit, Antoine. "Robust visual detection and tracking of complex objects : applications to space autonomous rendez-vous and proximity operations." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00931604.
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In this thesis, we address the issue of fully localizing a known object through computer vision, using a monocular camera, what is a central problem in robotics. A particular attention is here paid on space robotics applications, with the aims of providing a unified visual localization system for autonomous navigation purposes for space rendezvous and proximity operations. Two main challenges of the problem are tackled: initially detecting the targeted object and then tracking it frame-by-frame, providing the complete pose between the camera and the object, knowing the 3D CAD model of the object. For detection, the pose estimation process is based on the segmentation of the moving object and on an efficient probabilistic edge-based matching and alignment procedure of a set of synthetic views of the object with a sequence of initial images. For the tracking phase, pose estimation is handled through a 3D model-based tracking algorithm, for which we propose three different types of visual features, pertinently representing the object with its edges, its silhouette and with a set of interest points. The reliability of the localization process is evaluated by propagating the uncertainty from the errors of the visual features. This uncertainty besides feeds a linear Kalman filter on the camera velocity parameters. Qualitative and quantitative experiments have been performed on various synthetic and real data, with challenging imaging conditions, showing the efficiency and the benefits of the different contributions, and their compliance with space rendezvous applications.
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Demirdjian, David. "Le mouvement projectif : théorie et applications pour l'autocalibrage et la segmentation du mouvement." Phd thesis, Grenoble INPG, 2000. http://tel.archives-ouvertes.fr/tel-00590318.
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La vision stéréoscopique apparaît dans de nombreuses applications comme le moyen le plus évident pour obtenir des informations tridimensionnelles à partir d'images. Les approches employées reposent généralement sur des modèles euclidiens et nécessitent un étalonnage fort des systèmes stéréoscopiques utilisés, ce qui implique que les paramètres internes des caméras ainsi que la position relative entre les caméras doivent être connues. Or un étalonnage fort et précis nécessite généralement une intervention humaine. Cependant une aide extérieure n'est pas toujours possible et l'utilisation de systèmes faiblement étalonnés (systèmes dont seule la géométrie épipolaire est connue) apparaît alors comme une alternative. Un étalonnage faible est très facile à obtenir mais la difficulté est qu'alors les informations tridimensionnelles obtenues sont projectives et non plus euclidiennes. Ce document s'inscrit dans une approche basée sur un étalonnage faible et s'intéresse à l'étude d'un système stéréoscopique faiblement étalonné évoluant dans un environnement a priori inconnu. Il montre comment, en pratique, on peut tirer partie du mouvement d'un système stéréoscopique pour remonter à la structure métrique de la scène (par auto-étalonnage) et détecter des objets en mouvement. L'espace projectif est utilisé ici pour représenter l'information visuelle issue du système. En particulier, on étudie les transformations projectives 3D -appelées également homographies 3D- qui relient les reconstructions projectives d'une scène rigide. On s'intéresse au problème d'estimation de ces homographies 3D et on montre comment celles-ci entrent en jeu dans des applications telles que l'auto-étalonnage ou la segmentation du mouvement
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Sangi, P. (Pekka). "Object motion estimation using block matching with uncertainty analysis." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526200774.
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Abstract Estimation of 2-D motion is one of the fundamental problems in video processing and computer vision. This thesis addresses two general tasks in estimating projected motions of background and foreground objects in a scene: global motion estimation and motion based segmentation. The work concentrates on the study of the block matching method, and especially on those cases where the matching measure is based on the sum of squared or absolute displaced frame differences. Related techniques for performing the confidence analysis of local displacement are considered and used to improve the performance of the higher-level tasks mentioned. In general, local motion estimation techniques suffer from the aperture problem. Therefore, confidence analysis methods are needed which can complement motion estimates with information about their reliability. This work studies a particular form of confidence analysis which uses the evaluation of the match criterion for local displacement candidates. In contrast to the existing approaches, the method takes into account the local image gradient. The second part of the thesis presents a four-step feature based method for global motion estimation. For basic observations, it uses motion features which are combinations of image point coordinates, displacement estimates at those points, and representations of displacement uncertainty. A parametric form of uncertainty representation is computed exploiting the technique described in the first part of the thesis. This confidence information is used as a basis for weighting the features in motion estimation. Aspects of gradient based feature point selection are also studied. In the experimental part, the design choices of the method are compared, using both synthetic and real sequences. In the third part of the thesis, a technique for feature based extraction of background and foreground motions is presented. The new sparse segmentation algorithm performs competitive segmentation using both the spatial and temporal propagation of support information. The weighting of features exploits parametric uncertainty information which is experimentally shown to improve the performance of motion estimation. In the final part of the thesis, a novel framework for motion based object detection, segmentation, and tracking is developed. It uses a block grid based representation for segmentation and a particle filter based approach to motion estimation. Analysis techniques for obtaining the segmentation are described. Finally, the approach is integrated with the sparse motion segmentation and the combination of the methods is experimentally shown to increase both the efficiency of sampling and the accuracy of segmentationTiivistelmä Tässä väitöskirjassa tutkitaan yhtä videonkäsittelyn ja konenäön perusongelmaa, kaksiulotteisen liikkeen estimointia. Työ käsittelee kahta yleistä tehtävää taustan ja etualan kohteiden liikkeiden määrittämisessä: hallitsevan liikkeen estimointia ja liikepohjaista kuvan segmentointia. Tutkituissa ratkaisuissa lähtökohtana käytetään lohkosovitukseen perustuvaa paikallisen liikkeen määritystä, jossa sovituksen kriteerinä käytetään poikkeutettujen kehysten pikseliarvojen erotusta. Tähän liittyen tarkastellaan estimoinnin luotettavuuden analyysin tekniikoita ja näiden hyödyntämistä edellä mainittujen tehtävien ratkaisuissa. Yleensä ottaen paikallisen liikkeen estimointia vaikeuttaa apertuuriongelma. Tämän vuoksi tarvitaan analyysitekniikoita, jotka kykenevät antamaan täydentävää tietoa liike-estimaattien luotettavuudesta. Työn ensimmäisessä osassa kehitetty analyysimenetelmä käyttää lähtötietona lohkosovituksen kriteerin arvoja, jotka on saatu eri liikekandidaateille. Erotuksena aiempiin menetelmiin kehitetty ratkaisu ottaa huomioon kuvagradientin vaikutuksen. Työn toisessa osassa tutkitaan nelivaiheista piirrepohjaista ratkaisua hallitsevan liikkeen estimoimiseksi. Perushavaintoina mallissa käytetään liikepiirteitä, jotka koostuvat valittujen kuvapisteiden koordinaateista, näissä pisteissä lasketuista liike-estimaateista ja estimaattien epävarmuuden esityksestä. Jälkimmäinen esitetään parametrisessa muodossa käyttäen laskentaan työn ensimmäisessä osassa esitettyä menetelmää. Tätä epävarmuustietoa käytetään piirteiden painottamiseen hallitsevan liikkeen estimoinnissa. Lisäksi tutkitaan gradienttipohjaista piirteiden valintaa. Kokeellisessa osassa erilaisia suunnitteluvalintoja verrataan toisiinsa käyttäen synteettisiä ja todellisia kuvasekvenssejä. Väitöstyön kolmannessa osassa esitetään piirrepohjainen menetelmä taustan ja etualan kohteen liikkeiden erottamiseksi toisistaan. Algoritmi tekee analyysin kahta liikettä sisältävälle näkymälle käyttäen sekä spatiaalista että ajallista segmentointitiedon välittämistä. Piirteiden painotus hyödyntää epävarmuustietoa tässä yhteydessä, jonka osoitetaan kokeellisesti parantavan liike-estimoinnin suorituskykyä. Viimeisessä osassa kehitetään viitekehys liikepohjaisen kohteen ilmaisun, segmentoinnin ja seurannan toteutukselle. Se perustuu lohkopohjaiseen esitystapaan ja näytteistyksen soveltamiseen liikkeen estimoinnissa. Analyysitekniikka segmentoinnin määrittämiseksi esitellään. Lopuksi ratkaisu integroidaan työn kolmannessa osassa esitetyn menetelmän kanssa, ja menetelmien yhdistelmän osoitetaan kokeellisesti parantavan sekä näytteistyksen tehokkuutta että segmentoinnin tarkkuutta
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Lee, Jehoon. "Statistical and geometric methods for visual tracking with occlusion handling and target reacquisition." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43582.
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Computer vision is the science that studies how machines understand scenes and automatically make decisions based on meaningful information extracted from an image or multi-dimensional data of the scene, like human vision. One common and well-studied field of computer vision is visual tracking. It is challenging and active research area in the computer vision community. Visual tracking is the task of continuously estimating the pose of an object of interest from the background in consecutive frames of an image sequence. It is a ubiquitous task and a fundamental technology of computer vision that provides low-level information used for high-level applications such as visual navigation, human-computer interaction, and surveillance system.
The focus of the research in this thesis is visual tracking and its applications. More specifically, the object of this research is to design a reliable tracking algorithm for a deformable object that is robust to clutter and capable of occlusion handling and target reacquisition in realistic tracking scenarios by using statistical and geometric methods. To this end, the approaches developed in this thesis make extensive use of region-based active contours and particle filters in a variational framework. In addition, to deal with occlusions and target reacquisition problems, we exploit the benefits of coupling 2D and 3D information of an image and an object.
In this thesis, first, we present an approach for tracking a moving object based on 3D range information in stereoscopic temporal imagery by combining particle filtering and geometric active contours. Range information is weighted by the proposed Gaussian weighting scheme to improve segmentation achieved by active contours. In addition, this work present an on-line shape learning method based on principal component analysis to reacquire track of an object in the event that it disappears from the field of view and reappears later. Second, we propose an approach to jointly track a rigid object in a 2D image sequence and to estimate its pose in 3D space. In this work, we take advantage of knowledge of a 3D model of an object and we employ particle filtering to generate and propagate the translation and rotation parameters in a decoupled manner. Moreover, to continuously track the object in the presence of occlusions, we propose an occlusion detection and handling scheme based on the control of the degree of dependence between predictions and measurements of the system. Third, we introduce the fast level-set based algorithm applicable to real-time applications. In this algorithm, a contour-based tracker is improved in terms of computational complexity and the tracker performs real-time curve evolution for detecting multiple windows. Lastly, we deal with rapid human motion in context of object segmentation and visual tracking. Specifically, we introduce a model-free and marker-less approach for human body tracking based on a dynamic color model and geometric information of a human body from a monocular video sequence. The contributions of this thesis are summarized as follows:
1. Reliable algorithm to track deformable objects in a sequence consisting of 3D range data by combining particle filtering and statistics-based active contour models.
2. Effective handling scheme based on object's 2D shape information for the challenging situations in which the tracked object is completely gone from the image domain during tracking.
3. Robust 2D-3D pose tracking algorithm using a 3D shape prior and particle filters on SE(3).
4. Occlusion handling scheme based on the degree of trust between predictions and measurements of the tracking system, which is controlled in an online fashion.
5. Fast level set based active contour models applicable to real-time object detection.
6. Model-free and marker-less approach for tracking of rapid human motion based on a dynamic color model and geometric information of a human body.
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Barbosa, Daniel. "Automated assessment of cardiac morphology and function : An integrated B-spline framework for real-time segmentation and tracking of the left ventricle." Thesis, Lyon, INSA, 2013. http://www.theses.fr/2013ISAL0111.
Full textAbstract:
L’objectif principal de cette thèse est le développement de techniques de segmentation et de suivi totalement automatisées du ventricule gauche (VG) en RT3DE. Du fait de la nature difficile et complexe des données RT3DE, l’application directe des algorithmes classiques de vision par ordinateur est le plus souvent impossible. Les solutions proposées ont donc été formalisées et implémentées de sorte à satisfaire les contraintes suivantes : elles doivent permettre une analyse complètement automatique (ou presque) et le temps de calcul nécessaire doit être faible afin de pouvoir fonctionner en temps réel pour une utilisation clinique optimale. Dans ce contexte, nous avons donc proposé un nouveau cadre ou les derniers développements en segmentation d’images par ensembles de niveaux peuvent être aisément intégrés, tout en évitant les temps de calcul importants associés à ce type d’algorithmes. La validation clinique de cette approche a été effectuée en deux temps. Tout d’abord, les performances des outils développés ont été évaluées dans un contexte global se focalisant sur l’utilisation en routine clinique. Dans un second temps, la précision de la position estimée du contour du ventricule gauche a été mesurée. Enfin, les méthodes proposées ont été intégrées dans une suite logicielle utilisée à des fins de recherche. Afin de permettre une utilisation quotidienne efficace, des solutions conviviales ont été proposées incluant notamment un outil interactif pour corriger la segmentation du VGThe fundamental goal of the present thesis was the development of automatic strategies for left ventricular (LV) segmentation and tracking in RT3DE data. Given the challenging nature of RT3DE data, classical computer vision algorithms often face complications when applied to ultrasound. Furthermore, the proposed solutions were formalized and built to respect the following requirements: they should allow (nearly) fully automatic analysis and their computational burden should be low, thus enabling real-time processing for optimal online clinical use. With this in mind, we have proposed a novel segmentation framework where the latest developments in level-set-based image segmentation algorithms could be straightforwardly integrated, while avoiding the heavy computational burden often associated with level-set algorithms. Furthermore, a strong validation component was included in order to assess the performance of the proposed algorithms in realistic scenarios comprising clinical data. First, the performance of the developed tools was evaluated from a global perspective, focusing on its use in clinical daily practice. Secondly, also the spatial accuracy of the estimated left ventricular boundaries was assessed. As a final step, we aimed at the integration of the developed methods in an in-house developed software suite used for research purposes. This included user-friendly solutions for efficient daily use, namely user interactive tools to adjust the segmented left ventricular boundaries
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Villa, Jacopo. "Optical Navigation for Autonomous Approach of Unexplored Small Bodies." Thesis, KTH, Rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285863.
Full textAbstract:
This thesis presents an autonomous vision-based navigation strategy applicable to the approach phase of a small body mission, developed within the Robotics Section at NASA Jet Propulsion Laboratory. Today, the operations performed to approach small planetary bodies are largely dependent on ground support and human decision-making, which demand operational complexity and restrict the spectrum of achievable activities throughout the mission. In contrast, the autonomous pipeline presented here could be run onboard, without ground intervention. Using optical data only, the pipeline estimates the target body's rotation, pole, shape, and performs identification and tracking of surface landmarks, for terrain relative navigation. An end-to-end simulation is performed to validate the pipeline, starting from input synthetic images and ending with an orbit determination solution. As a case study, the approach phase of the Rosetta mission is reproduced, and it is concluded that navigation performance is in line with the ground-based state-of-the-art. Such results are presented in detail in the paper attached in the appendix, which presents the pipeline architecture and navigation analysis. This thesis manuscript aims to provide additional context to the appended paper, further describing some implementation details used for the approach simulations.Detta examensarbete presenterar en strategi för ett autonomt visionsbaserat navigationssystem för att närma sig en liten himlakropp. Strategin har utvecklats av robotikavdelningen vid NASA Jet Propulsion Laboratory i USA. Nuvarande system som används för att närma sig en liten himlakropp bygger till största delen på markstationer och mänskligt beslutsfattande, vilka utgör komplexa rutiner och begränsar spektrumet av möjliga aktiviteter under rymduppdraget. I jämförelse, det autonoma system presenterat i denna rapport är utformat för att köras helt från rymdfarkosten och utan krav på kontakt med markstationer. Genom att använda enbart optisk information uppskattar systemet himlakroppens rotation, poler och form samt genomför en identifiering och spårning av landmärken på himlakroppens yta för relativ terrängnavigering. En simulering har genomförts för att validera det autonoma navigationssystemet. Simuleringen utgick ifrån bilder av himlakroppen och avslutades med en lösning på banbestämningsproblemet. Fasen då rymdfarkosten i ESA:s Rosetta-rymduppdrag närmar sig kometen valdes som fallstudie för simuleringen och slutsatsen från denna fallstudie var att systemets autonoma navigationsprestanda var i linje med toppmoderna system. Den detaljerade beskrivningen av det autonoma systemet och resultaten från studien har presenterats i ett konferensbidrag, som ingår som bilaga till rapporten. Inledningen av rapporten syftar till att förtydliga bakgrunden och implementering som komplement till innehållet i bilagan.
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Dorini, Leyza Elmeri Baldo. "Propagação de pontos caracteristicos e suas incertezas utilizando a transformada unscented." [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276486.
Full textAbstract:
Orientador: Siome Klein GoldensteinDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-06T08:17:58Z (GMT). No. of bitstreams: 1 Dorini_LeyzaElmeriBaldo_M.pdf: 1659888 bytes, checksum: 3ac1fe51a9d8159f6fa5df97cb4f52bc (MD5) Previous issue date: 2006
Resumo: O correto estabelecimento de correspondências entre imagens tomadas de diferentes pontos de vista é um problema fundamental na área de visão computacional, sendo base para diversas tarefas de alto nível, tais como reconstrução 3D e análise de movimento. A grande maioria dos algoritmos de rastreamento de características não possui uma incerteza associada a posição estimada das características sendo rastreadas, informação esta de extrema importância, considerando sua vasta aplicabilidade. Exatamente este o foco principal deste trabalho, onde introduzimos um framework genérico que expande algoritmos de rastreamento de tal forma que eles possam propagar também informações de incerteza. Neste trabalho, por questão de simplicidade, utilizamos o algoritmo de rastreamento de características Kanade-Lucas-Tomasi (KLT) para demonstrar as vantagens do nosso método, denominado Unscented Feature Tracking (UFT). A abordagem consiste na introdução de Variáveis Aleatórias Gaussianas (GRVs) para a representação da localização dos pontos característicos, e utiliza a Transformada Unscented com Escala (SUT) para propagar e combinar GRVs. Mostramos uma aplicação do UFT em um procedimento de bundle adjustment, onde a função custo leva em conta a informação das GRVs, fornecendo melhores estimativas. O método é robusto, considerando que identifica e descarta anomalias, que podem comprometer de maneira expressiva o resultado de tarefas que utilizam as correspondências. Experimentos com seqüências de imagens reais e sintéticas comprovam os benefícios do método proposto
Abstract: To determine reliable correspondences between a pair of images is a fundamental problem in the computer vision community. It is the foundation of several high level tasks, such as 3D reconstruction and motion analysis. Although there are many feature tracking algorithms, most of them do not maintain information about the uncertainty of the feature locations' estimates. This information is very useful, since large errors can disturb the results of the correspondence-based tasks. This is the goal of this work, a new generic framework that augments feature tracking algorithms so that they also propagate uncertainty information. In this work, we use the well-known Kanade-Lucas-Tomasi (KLT) feature tracker to demonstrate the benefits of our method, called Unscented Feature Tracking (UFT). The approach consists on the introduction of Gaussian Random Variables (GRVs) for the representation of the features' locations, and on the use of the Scaled Unscented Transform (SUT) to propagate and combine GRVs. We also describe an improved bundle adjustment procedure as an application, where the cost function takes into account the information of the GRVs, and provides better estimates. Experiments with real and synthetic images confirm that UFT improves the quality of the feature tracking process and is a robust method for detect and reject outliers
Mestrado
Visão Computacional
Mestre em Ciência da Computação
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Pati, Nishikanta. "Occlusion Tolerant Object Recognition Methods for Video Surveillance and Tracking of Moving Civilian Vehicles." Thesis, University of North Texas, 2007. https://digital.library.unt.edu/ark:/67531/metadc5133/.
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Recently, there is a great interest in moving object tracking in the fields of security and surveillance. Object recognition under partial occlusion is the core of any object tracking system. This thesis presents an automatic and real-time color object-recognition system which is not only robust but also occlusion tolerant. The intended use of the system is to recognize and track external vehicles entered inside a secured area like a school campus or any army base. Statistical morphological skeleton is used to represent the visible shape of the vehicle. Simple curve matching and different feature based matching techniques are used to recognize the segmented vehicle. Features of the vehicle are extracted upon entering the secured area. The vehicle is recognized from either a digital video frame or a static digital image when needed. The recognition engine will help the design of a high performance tracking system meant for remote video surveillance.
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Rackham, Thomas. "Ultrasound segmentation tools and their application to assess fetal nutritional health." Thesis, University of Oxford, 2016. http://ora.ox.ac.uk/objects/uuid:5d102b18-dd32-4004-8aa5-b04242139daa.
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Maternal diet can have a great impact on the health and development of the fetus. Poor fetal nutrition has been linked to the development of a set of conditions in later life, such as coronary heart disease, type 2 diabetes and hypertension, while restricted growth can result in hypogylcemia, hypocalcemia, hypothermia, polycythemia, hyperbilirubinemia and cerebral palsy. High alcohol consumption during pregnancy can result in Fetal Alcohol Syndrome, a condition that can cause growth retardation, lowered intelligence and craniofacial defects. Current biometric assessment of the fetus involves size-based measures which may not accurately portray the state of fetal development, since they cannot differentiate cases of small-but-healthy or large-but-unhealthy fetuses. This thesis aims to outline a set of more appropriate measures of accurately capturing the state of fetal development. Specifically, soft tissue area and liver volume measurement are examined, followed by facial shape characterisation. A number of tools are presented which aim to allow clinicians to achieve accurate segmentations of these landmark regions. These are modifications on the Live Wire algorithm, an interactive segmentation method in which the user places a number of anchor points and a minimum cost path is calculated between the previous anchor point and the cursor. This focuses on giving the clinician intuitive control over the exact position of the segmented contour. These modifications are FA-S Live Wire, which utilises Feature Asymmetry and a weak shape constraint, ASP Live Wire, which is a 3D expansion of Live Wire, and FA-O Live Wire, which uses Feature Asymmtery and Local Orientation to guide the segmentation process. These have been designed with each of the specific biometric landmarks in mind. Finally, a method of characterising fetal face shape is proposed, using a combination of the segmentation methods described here and a simple shape model with a parameterised b-spline meshing approach to facial surface representation.
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Lee, Yeongseon. "Bayesian 3D multiple people tracking using multiple indoor cameras and microphones." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29668.
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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.Committee Chair: Rusell M. Mersereau; Committee Member: Biing Hwang (Fred) Juang; Committee Member: Christopher E. Heil; Committee Member: Georgia Vachtsevanos; Committee Member: James H. McClellan. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Trejo, Guerrero Sandra. "Model-Based Eye Detection and Animation." Thesis, Linköping University, Department of Electrical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7059.
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In this thesis we present a system to extract the eye motion from a video stream containing a human face and applying this eye motion into a virtual character. By the notation eye motion estimation, we mean the information which describes the location of the eyes in each frame of the video stream. Applying this eye motion estimation into a virtual character, we achieve that the virtual face moves the eyes in the same way than the human face, synthesizing eye motion into a virtual character. In this study, a system capable of face tracking, eye detection and extraction, and finally iris position extraction using video stream containing a human face has been developed. Once an image containing a human face is extracted from the current frame of the video stream, the detection and extraction of the eyes is applied. The detection and extraction of the eyes is based on edge detection. Then the iris center is determined applying different image preprocessing and region segmentation using edge features on the eye picture extracted.
Once, we have extracted the eye motion, using MPEG-4 Facial Animation, this motion is translated into the Facial Animation arameters (FAPs). Thus we can improve the quality and quantity of Facial Animation expressions that we can synthesize into a virtual character.
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Kong, Longbo. "Accurate Joint Detection from Depth Videos towards Pose Analysis." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157524/.
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Joint detection is vital for characterizing human pose and serves as a foundation for a wide range of computer vision applications such as physical training, health care, entertainment. This dissertation proposed two methods to detect joints in the human body for pose analysis. The first method detects joints by combining body model and automatic feature points detection together. The human body model maps the detected extreme points to the corresponding body parts of the model and detects the position of implicit joints. The dominant joints are detected after implicit joints and extreme points are located by a shortest path based methods. The main contribution of this work is a hybrid framework to detect joints on the human body to achieve robustness to different body shapes or proportions, pose variations and occlusions. Another contribution of this work is the idea of using geodesic features of the human body to build a model for guiding the human pose detection and estimation. The second proposed method detects joints by segmenting human body into parts first and then detect joints by making the detection algorithm focusing on each limb. The advantage of applying body part segmentation first is that the body segmentation method narrows down the searching area for each joint so that the joint detection method can provide more stable and accurate results.
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Sundaramoorthi, Ganesh. "Global Optimizing Flows for Active Contours." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16145.
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This thesis makes significant contributions to the object detection problem in computer vision. The object detection problem is, given a digital image of a scene, to detect the relevant object in the image. One technique for performing object detection, called ``active contours,' optimizes a constructed energy that is defined on contours (closed curves) and is tailored to image features. An optimization method can be used to perform the optimization of the energy, and thereby deform an initially placed contour to the relevant object. The typical optimization technique used in almost every active contour paper is evolving the contour by the energy's gradient descent flow, i.e., the steepest descent flow, in order to drive the initial contour to (hopefully) the minimum curve. The problem with this technique is that often times the contour becomes stuck in a sub-optimal and undesirable local minimum of the energy. This problem can be partially attributed to the fact that the gradient flows of these energies make use of only local image and contour information. By local, we mean that in order to evolve a point on the contour, only information local to that point is used. Therefore, in this thesis, we introduce a new class of flows that are global in that the evolution of a point on the contour depends on global information from the entire curve. These flows help avoid a number of problems with traditional flows including helping in avoiding
undesirable local minima. We demonstrate practical applications of these flows for the object detection problem, including applications
to both image segmentation and visual object tracking.
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Martinez, Pujol Oriol. "Template tracking of articulated objects using active contours." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/373919.
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En aquesta dissertació es fusionen dos dels temes tradicionals de la Visió per Computador: la segmentació i el seguiment d'objectes. Per a la segmentació s'utilitzen mètodes basats en "Active Contours (AC)" i per al seguiment mètodes basats en "templates" o patrons. El nostre objectiu és combinar-los per tal de crear mètodes robustos i eficients a l'hora de segmentar i seguir objectes articulats o deformables. Al capítol 1 es revisa el marc teòric dels AC i s'aplica en la segmentació de cossos i amenaces (com explosius o pistoles) que estan amagades darrera la roba en imatges MilliMeter-Waves (MMW). Al capítol 2 es revisen dos dels marcs principals de seguiment de patrons: el flux òptic de Lucas-Kanade i els filtres de partícules, i es combinen amb la segmentació mitjançant AC per tal de crear un mètode robust i eficient capaç de seguir objectes articulats o deformables sense informació a priori. Finalment, al capítol 3 es donen les claus per introduir informació a priori d'una manera robusta i eficient dins del marc del seguiment de patrons utilitzant AC.In this dissertation we fuse two of the traditional topics in Computer Vision: object segmentation and tracking. For segmentation we use the Active Contours (AC) framework and for tracking we use the Template Tracking (TT) scheme. Our aim is to combine them to create efficient and robust methods to segment and track articulated or deformable objects. In Chapter 1, we review the AC framework and we apply it over MilliMeter-Waves (MMW) images to segment bodies and concealed threats (such as explosives or guns) behind their wearing clothes. In Chapter 2 we review two of the main trends of TT methods: Lucas-Kanade optical flow and particle filters. Moreover, we combine them with an AC method to create a robust tracker for articulated or deformable objects without using prior shape information. Finally, in Chapter 3 we give the clues of how to efficiently introduce shape priors into the TT framework using AC methods.
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Choi, Changhyun. "Visual object perception in unstructured environments." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53003.
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As robotic systems move from well-controlled settings to increasingly unstructured environments, they are required to operate in highly dynamic and cluttered scenarios. Finding an object, estimating its pose, and tracking its pose over time within such scenarios are challenging problems. Although various approaches have been developed to tackle these problems, the scope of objects addressed and the robustness of solutions remain limited. In this thesis, we target a robust object perception using visual sensory information, which spans from the traditional monocular camera to the more recently emerged RGB-D sensor, in unstructured environments. Toward this goal, we address four critical challenges to robust 6-DOF object pose estimation and tracking that current state-of-the-art approaches have, as yet, failed to solve.
The first challenge is how to increase the scope of objects by allowing visual perception to handle both textured and textureless objects. A large number of 3D object models are widely available in online object model databases, and these object models provide significant prior information including geometric shapes and photometric appearances. We note that using both geometric and photometric attributes available from these models enables us to handle both textured and textureless objects. This thesis presents our efforts to broaden the spectrum of objects to be handled by combining geometric and photometric features.
The second challenge is how to dependably estimate and track the pose of an object despite the clutter in backgrounds. Difficulties in object perception rise with the degree of clutter. Background clutter is likely to lead to false measurements, and false measurements tend to result in inaccurate pose estimates. To tackle significant clutter in backgrounds, we present two multiple pose hypotheses frameworks: a particle filtering framework for tracking and a voting framework for pose estimation.
Handling of object discontinuities during tracking, such as severe occlusions, disappearances, and blurring, presents another important challenge. In an ideal scenario, a tracked object is visible throughout the entirety of tracking. However, when an object happens to be occluded by other objects or disappears due to the motions of the object or the camera, difficulties ensue. Because the continuous tracking of an object is critical to robotic manipulation, we propose to devise a method to measure tracking quality and to re-initialize tracking as necessary.
The final challenge we address is performing these tasks within real-time constraints. Our particle filtering and voting frameworks, while time-consuming, are composed of repetitive, simple and independent computations. Inspired by that observation, we propose to run massively parallelized frameworks on a GPU for those robotic perception tasks which must operate within strict time constraints.
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Usher, Kane. "Visual homing for a car-like vehicle." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16309/.
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This thesis addresses the pose stabilization of a car-like vehicle using omnidirectional visual feedback. The presented method allows a vehicle to servo to a pre-learnt target pose based on feature bearing angle and range discrepancies between the vehicle's current view of the environment and that seen at the learnt location. The best example of such a task is the use of visual feedback for autonomous parallel-parking of an automobile. Much of the existing work in pose stabilization is highly theoretical in nature with few examples of implementations on 'real' vehicles, let alone vehicles representative of those found in industry. The work in this thesis develops a suitable test platform and implements vision-based pose stabilization techniques. Many of the existing techniques were found to fail due to vehicle steering and velocity loop dynamics, and more significantly, with steering input saturation. A technique which does cope with the characteristics of 'real' vehicles is to divide the task into predefined stages, essentially dividing the state space into sub-manifolds. For a car-like vehicle, the strategy used is to stabilize the vehicle to the line which has the correct orientation and contains the target location. Once on the line, the vehicle then servos to the desired pose. This strategy can accommodate velocity and steering loop dynamics, and input saturation. It can also allow the use of linear control techniques for system analysis and tuning of control gains. To perform pose stabilization, good estimates of vehicle pose are required. A simple, yet robust, method derived from the visual homing literature is to sum the range vectors to all the landmarks in the workspace and divide by the total number of landmarks--the Improved Average Landmark Vector. By subtracting the IALV at the target location from the currently calculated IALV, an estimate of vehicle pose is obtained. In this work, views of the world are provided by an omnidirectional camera, while a magnetic compass provides a reference direction. The landmarks used are red road cones which are segmented from the omnidirectional colour images using a pre-learnt, two-dimensional lookup table of their colour profile. Range to each landmark is estimated using a model of the optics of the system, based on a flat-Earth assumption. A linked-list based method is used to filter the landmarks over time. Complementary filtering techniques, which combine the vision data with vehicle odometry, are used to improve the quality of the measurements.
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Groulík, Tomáš. "Kamerový subsystém mobilního robotu Minidarpa." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218317.
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Master`s thesis is focused on mobile robotics and computer vision. There is briefly introduced a library of functions for image processing OpenCV. Then it deals with image processing and navigation of mobile robots using image data. There are described segmentation methods and methods for navigating through feature points.