Relevant bibliographies by topics / Landmark Estimation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Landmark Estimation'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Landmark Estimation"

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Sakai, Atsushi, Teppei Saitoh, and Yoji Kuroda. "Robust Landmark Estimation and Unscented Particle Sampling for SLAM in Dynamic Outdoor Environment." Journal of Robotics and Mechatronics 22, no. 2 (April 20, 2010): 140–49. http://dx.doi.org/10.20965/jrm.2010.p0140.

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In this paper, we propose a set of techniques for accurate and practical Simultaneous Localization And Mapping (SLAM) in dynamic outdoor environments. The techniques are categorized into Landmark estimation and Unscented particle sampling. Landmark estimation features stable feature detection and data management for estimating landmarks accurately, robustly, and at a low-calculation cost. The stable feature detection removes dynamic objects and sensor noise with scan subtraction, detects feature points sparsely and evenly, and sets data association parameters with landmark density. The data management calculates landmark existence probability and spurious landmarks are removed, utilizes landmark exclusivity for data association, and predicts importance weights using the observation range. Unscented particle sampling is based on Unscented Transformation for accurate SLAM. Simulation results of SLAM using our landmark estimation and experimental results of our SLAM in dynamic outdoor environments are presented and discussed. The results show that our landmark estimation decrease SLAM calculation time and maximum position error by 80% compared to conventional landmark estimation, and position estimation of SLAM with Unscented particle sampling ismore accurate than FastSLAM2.0 in dynamic outdoor environments.
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Liu, Shengli, Xiaowen Zhu, Zewei Cao, and Gang Wang. "Deep 1D Landmark Representation Learning for Space Target Pose Estimation." Remote Sensing 14, no. 16 (August 18, 2022): 4035. http://dx.doi.org/10.3390/rs14164035.

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Monocular vision-based pose estimation for known uncooperative space targets plays an increasingly important role in on-orbit operations. The existing state-of-the-art methods of space target pose estimation build the 2D-3D correspondences to recover the space target pose, where space target landmark regression is a key component of the methods. The 2D heatmap representation is the dominant descriptor in landmark regression. However, its quantization error grows dramatically under low-resolution input conditions, and extra post-processing is usually needed to compute the accurate 2D pixel coordinates of landmarks from heatmaps. To overcome the aforementioned problems, we propose a novel 1D landmark representation that encodes the horizontal and vertical pixel coordinates of a landmark as two independent 1D vectors. Furthermore, we also propose a space target landmark regression network to regress the locations of landmarks in the image using 1D landmark representations. Comprehensive experiments conducted on the SPEED dataset show that the proposed 1D landmark representation helps the proposed space target landmark regression network outperform existing state-of-the-art methods at various input resolutions, especially at low resolutions. Based on the 2D landmarks predicted by the proposed space target landmark regression network, the error of space target pose estimation is also smaller than existing state-of-the-art methods under all input resolution conditions.
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Fujii, Hajime, Yoshinobu Ando, Takashi Yoshimi, and Makoto Mizukawa. "Shape Recognition of Metallic Landmark and its Application to Self-Position Estimation for Mobile Robot." Journal of Robotics and Mechatronics 22, no. 6 (December 20, 2010): 718–25. http://dx.doi.org/10.20965/jrm.2010.p0718.

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This paper proposes a method of improving selfposition estimation accuracy with metallic landmarks for mobile robots. Many methods of the past selfposition estimation researches have used GPS, laserrange scanners, and CCD cameras, but have been unable to obtain landmark information correctly due to environmental factors. Metallic landmarks are useful in environments where conventional sensors do not work well. Self-position estimation accuracy is thus increased by combining metallic landmark information with that from other equipment.
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Floreskul, Volodymyr, Konstantin Tretyakov, and Marlon Dumas. "Memory-Efficient Fast Shortest Path Estimation in Large Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 8, no. 1 (May 16, 2014): 91–100. http://dx.doi.org/10.1609/icwsm.v8i1.14532.

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As the sizes of contemporary social networks surpass billions of users, so grows the need for fast graph algorithms to analyze them. A particularly important basic operation is the computation of shortest paths between nodes. Classical exact algorithms for this problem are prohibitively slow on large graphs, which motivates the development of approximate methods. Of those, landmark-based methods have been actively studied in recent years. Landmark-based estimation methods start by picking a fixed set of landmark nodes, precomputing the distance from each node in the graph to each landmark, and storing the precomputed distances in a data structure. Prior work has shown that the number of landmarks required to achieve a given level of precision grows with the size of the graph. Simultaneously, the size of the data structure is proportional to the product of the size of the graph and the number of landmarks. In this work we propose an alternative landmark-based distance estimation approach that substantially reduces space requirements by means of pruning: computing distances from each node to only a small subset of the closest landmarks. We evaluate our method on the DBLP, Orkut, Twitter and Skype social networks and demonstrate that the resulting estimation algorithms are comparable in query time and potentially superior in approximation quality to equivalent non-pruned landmark-based methods, while requiring less memory or disk space.
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Fong, Li Wei, Pi Ching Lou, and Ke Jia Tang. "Vehicle Kinematic State Estimation Using Passive Sensor Fusion Approach." Applied Mechanics and Materials 271-272 (December 2012): 1709–12. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.1709.

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The main issue addressed here is that of estimating the kinematic state components of a vehicle in autonomous navigation using landmark angle-only measurements from an onboard passive sensor. The estimates of the absolute position and velocity of the vehicle are provided by a hybrid coordinate fusion filter. The hierarchical architecture of the filter which consists of a group of local processors and a global processor is developed for improving estimation accuracy. In each local processor, an extended Kalman filter uses hybrid information from the reference Cartesian coordinate system and the modified polar coordinate system for state and state error covariance extrapolation and updating. In the global processor, a weighted least squares estimator is utilized to combine the outputs of local processors to form a global estimate. By using only two landmarks simulation results show that proposed algorithm improves the estimation accuracy drastically.
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Mohd Shah, Hairol Nizam, Zalina Kamis, Azhar Ahmad, Mohd Rizuan Baharon, Muhd Akmal Noor Rajikon, and Kang Hui Hwa. "Vision Based Position Control for Vertical Take-off and Landing (VTOL) Using One Singular Landmark." Modern Applied Science 13, no. 9 (August 22, 2019): 33. http://dx.doi.org/10.5539/mas.v13n9p33.

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This project presents a vision based position control for Vertical Take-off and Landing (VTOL) to recognise a singular landmark for landing and take-off. Position control can provide safe flight and an accurate navigation. The circle landmark which used is an artificial landmark at known locations in an environment. Initially, a camera mounted on VTOL facing downward detecting landmarks in environments. A single circle used as landmark and VTOL will be control the position to reach the landmark. The images from the down-looking camera provided vision data to estimates position of VTOL from landmark. A mathematical method based on projective geometry using to locate VTOL on desired landmark from projected point in capture image. By compute the x-y coordinates of the VTOL with respect to landmark, height of camera above landmark will be obtained. VTOL can localize itself in known environment with pose estimation from landmark. The graphic user interface system (GUI) generate by MATLAB software is used to communicate with VTOL to control the VTOL position
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Chen, Yao Chang, Ta Ming Shih, and Chung Ho Wang. "Stereo Vision Specific Observation Model for EKF-Based SLAM." Applied Mechanics and Materials 373-375 (August 2013): 238–41. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.238.

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This work addresses a new probabilistic observation model for a stereo simultaneous localization and mapping (SLAM) system within the standard Extended-Kalman filter (EKF) framework. The observation modal was derived by using the inverse depth parameterization as the landmark modal, and contributes to both bearing and range information into the EKF estimation. In this way the inherently non-linear problem cause by the projection equations is resolved and real depth uncertainty distribution of landmarks features can be accurately estimated. The system was demonstrated with real-world outdoor data. Analysis results show landmark feature depth estimation is more stable and the uncertainty noise converges faster than the traditional approach.
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Hsu, Chen-Chien, Cheng-Kai Yang, Yi-Hsing Chien, Yin-Tien Wang, Wei-Yen Wang, and Chiang-Heng Chien. "Computationally efficient algorithm for vision-based simultaneous localization and mapping of mobile robots." Engineering Computations 34, no. 4 (June 12, 2017): 1217–39. http://dx.doi.org/10.1108/ec-05-2015-0123.

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Purpose FastSLAM is a popular method to solve the problem of simultaneous localization and mapping (SLAM). However, when the number of landmarks present in real environments increases, there are excessive comparisons of the measurement with all the existing landmarks in each particle. As a result, the execution speed will be too slow to achieve the objective of real-time navigation. Thus, this paper aims to improve the computational efficiency and estimation accuracy of conventional SLAM algorithms. Design/methodology/approach As an attempt to solve this problem, this paper presents a computationally efficient SLAM (CESLAM) algorithm, where odometer information is considered for updating the robot’s pose in particles. When a measurement has a maximum likelihood with the known landmark in the particle, the particle state is updated before updating the landmark estimates. Findings Simulation results show that the proposed CESLAM can overcome the problem of heavy computational burden while improving the accuracy of localization and mapping building. To practically evaluate the performance of the proposed method, a Pioneer 3-DX robot with a Kinect sensor is used to develop an RGB-D-based computationally efficient visual SLAM (CEVSLAM) based on Speeded-Up Robust Features (SURF). Experimental results confirm that the proposed CEVSLAM system is capable of successfully estimating the robot pose and building the map with satisfactory accuracy. Originality/value The proposed CESLAM algorithm overcomes the problem of the time-consuming process because of unnecessary comparisons in existing FastSLAM algorithms. Simulations show that accuracy of robot pose and landmark estimation is greatly improved by the CESLAM. Combining CESLAM and SURF, the authors establish a CEVSLAM to significantly improve the estimation accuracy and computational efficiency. Practical experiments by using a Kinect visual sensor show that the variance and average error by using the proposed CEVSLAM are smaller than those by using the other visual SLAM algorithms.
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D’Amelio, Richard, and Thomas J. Dunn. "Revisiting the Santa Barbara sense of direction scale, mental rotations, and gender differences in spatial orientation." PsyPag Quarterly 1, no. 115 (June 2020): 7–10. http://dx.doi.org/10.53841/bpspag.2020.1.115.7.

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Angular direction estimation to landmarks of varying distance in the physical environment was utilised to investigate the ecological validity of the Santa Barbara sense of direction scale (SBSOD). Two- and three-dimensional MR measures were included to enable further the scale applicability. Results showed a moderate correlation between SBSOD and angular deviation from landmarks in the immediate landscape, but not with local or distant landmarks. Moreover, the findings suggest that skills which underlie three-dimensional MR better relate to pointing accuracy (PA) of distant landmarks and the cardinal direction, North. Results also showed a gender-related systematic biases in landmark estimation.
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Chen, Haiwen, Jin Chen, Zhuohuai Guan, Yaoming Li, Kai Cheng, and Zhihong Cui. "Stereovision-Based Ego-Motion Estimation for Combine Harvesters." Sensors 22, no. 17 (August 25, 2022): 6394. http://dx.doi.org/10.3390/s22176394.

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Ego-motion estimation is a foundational capability for autonomous combine harvesters, supporting high-level functions such as navigation and harvesting. This paper presents a novel approach for estimating the motion of a combine harvester from a sequence of stereo images. The proposed method starts with tracking a set of 3D landmarks which are triangulated from stereo-matched features. Six Degree of Freedom (DoF) ego motion is obtained by minimizing the reprojection error of those landmarks on the current frame. Then, local bundle adjustment is performed to refine structure (i.e., landmark positions) and motion (i.e., keyframe poses) jointly in a sliding window. Both processes are encapsulated into a two-threaded architecture to achieve real-time performance. Our method utilizes a stereo camera, which enables estimation at true scale and easy startup of the system. Quantitative tests were performed on real agricultural scene data, comprising several different working paths, in terms of estimating accuracy and real-time performance. The experimental results demonstrated that our proposed perception system achieved favorable accuracy, outputting the pose at 10 Hz, which is sufficient for online ego-motion estimation for combine harvesters.
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Dissertations / Theses on the topic "Landmark Estimation"

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Soh, Ling Min. "Recognition using tagged objects." Thesis, University of Surrey, 2000. http://epubs.surrey.ac.uk/844110/.

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This thesis describes a method for the recognition of objects in an unconstrained environment with a widely ranging illumination, imaged from unknown view points and complicated background. The general problem is simplified by placing specially designed patterns on the object that allows us to solve the pose determination problem easily. There are several key components involved in the proposed recognition approach. They include pattern detection, pose estimation, model acquisition and matching, searching and indexing the model database. Other crucial issues pertaining to the individual components of the recognition system such as the choice of the pattern, the reliability and accuracy of the pattern detector, pose estimator and matching and the speed of the overall system are addressed. After establishing the methodological framework, experiments are carried out on a wide range of both synthetic and real data to illustrate the validity and usefulness of the proposed methods. The principal contribution of this research is a methodology for Tagged Object Recognition (TOR) in unconstrained conditions. A robust pattern (calibration chart) detector is developed for off-the-shelf use. To empirically assess the effectiveness of the pattern detector and the pose estimator under various scenarios, simulated data generated using a graphics rendering process is used. This simulated data provides ground truth which is difficult to obtain in projected images. Using the ground truth, the detection error, which is usually ignored, can be analysed. For model matching, the Chamfer matching algorithm is modified to get a more reliable matching score. The technique facilitates reliable Tagged Object Recognition (TOR). Finally, the results of extensive quantitative and qualitative tests are presented that show the plausibility of practical use of Tagged Object Recognition (TOR). The features characterising the enabling technology developed are the ability to a) recognise an object which is tagged with the calibration chart, b) establish camera position with respect to a landmark and c) test any camera calibration and 3D pose estimation routines, thus facilitating future research and applications in mobile robots navigations, 3D reconstruction and stereo vision.
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Luo, Chong. "Driver's Gaze Zone Estimation in Realistic Driving Environment by Kinect." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38076.

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Driver's distraction is one of the main areas, which researchers are focusing on, in design of Advanced Drivers Assistance Systems (ADASs). Head pose and eye-gaze direction are two reliable indicators of a driver's gaze and the current focus of attention. Compared with other methods that make use of head pose only, methods that combine eye information can achieve higher accuracy. The naturalistic driving environment always presents unique challenges (e.g., unstable illumination, jolts, etc.) to video-based gaze estimation and tracking systems. Some methods can achieve relatively high proficiency in the stationary laboratory environment, but they may not be suitable for the unstable driving environment. In addition, performing in real time or near-real time is another consideration for gaze estimation in an ADAS. Therefore, these special challenges need to be overcome to design ADASs. In this thesis, we proposed a new driver's gaze zone estimation framework designed for the naturalistic driving environment. The framework combines head and eye information to estimate the gaze zone of the driver in both daytime and nighttime. The framework is composed of five main components: Facial Landmark Detection, Head Pose Estimation, Iris Center Detection, Upper Eyelid Information Extraction, and Gaze Zone Estimation. First, Constrained Local Neural Field (CLNF) is applied to obtain the facial landmarks in the image plane and the 3D model of the face in the object frame. In addition, extracting region of interest (ROI) is utilized as an optimization strategy for CLNF facial landmark detection. Second, head pose estimation can be regarded as a Perspective-n-Point (PnP) problem. Levenberg-Marquardt optimization method is used to solve the PnP problem based on the 2D landmark locations in the image plane and their corresponding 3D locations in the object frame. Third, a regression model-based method is employed to obtain the iris center from eye landmarks detected in the previous part. For upper eyelid information extraction, a quadratic function is utilized to model the upper eyelid, and the second-order coefficient is extracted. Finally, the head pose and the eye information are combined to form a feature vector, and Random Decision Forest classifier is utilized to estimate the current gaze zone of the driver from the feature vector extracted. The experiment is carried out in the realistic driving environment in both daytime and nighttime with three volunteers by Kinect sensor V2 for Windows that is put at the back of windshield. Weighted and unweighted accuracy are utilized as evaluation metrics in gaze zone estimation. Weighted accuracy evaluates gaze zones with different significance while unweighted accuracy treats each gaze zone equally. Experiment results show that the gaze zone estimation framework proposed in this work has better performance compared to the reference in the daytime. The weighted and unweighted accuracy of gaze zone estimation reach 96.6% and 95.0% for daytime, respectively. For nighttime, the weighted and unweighted accuracy can reach 96% and 91.4%.
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Zhao, Sanqiang. "On Sparse Point Representation for Face Localisation and Recognition." Thesis, Griffith University, 2009. http://hdl.handle.net/10072/366629.

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Automatic face recognition has been an active research field during the last few decades. Existing face recognition systems have demonstrated acceptable recognition performance under controlled conditions. However, practical and robust face recognition which is tolerant to various interferential variations remains a difficult and unsolved problem in the research community. In the first part of this thesis, we propose to use the concept of sparse point representation to address four important challenges in face recognition: wider-range tolerance to pose variation, face misalignment, facial landmark localisation and head pose estimation. The sparse point representation can be classified into two different categories. In the first category, equal numbers of feature points are predefined on different individuals. Each feature point refers to a specific physical location on a face while all the feature points have explicit correspondence across different individuals. In the second category, a set of feature points are detected at different locations with discriminative information content on a face image. Both the number and the positions of the feature points are varied from person to person such that diverse facial characteristics of different individuals can be represented. Based on the first category of sparse point representation, we propose a new Constrained Profile Model (CPM) to form an efficient facial landmark localisation framework. We also propose a novel Elastic Energy Model (EEM) to automatically conduct head pose estimation. Based on the second category of sparse point representation, we propose a new Textural Hausdorff Distance (THD), which has demonstrated a considerably wider range of tolerance against both in-depth head rotation and face misalignment. In the second part of this thesis, we focus on recently proposed micropattern based approaches which have proven to outperform classical face recognition methods and provided a new way of investigation into face analysis. We first apply a new Multidirectional Binary Pattern (MBP) representation upon sparse points to establish point correspondences for face recognition. We further propose an enhanced Sobel-LBP operator for face representation, which has demonstrated better performance than the original Local Binary Pattern (LBP). We finally present a novel high-order Local Derivative Pattern (LDP) for face recognition, which can capture more detailed and discriminative information than the first-order local pattern used in LBP. It should be noted that the concept of LDP for face recognition was pioneered by Dr. Baochang Zhang, but we have significantly extended and elaborated this concept. We have extended the concept of LDP from its original usage on Gabor phase features only to much more generalised definition on gray-level images. We have rewritten and enlarged the original draft of his manuscript. Some of the experiments were also implemented and reported by us. In the third part of this thesis, we pay attention to the representation of 'Average Face', which was newly published on Science and claimed to be capable of dramatically improving performance of face recognition systems. To reveal its working mechanism, we conduct a comparative study to observe its effectiveness on holistic and local face recognition approaches. Our experimental results reveal that the process of face averaging does not necessarily improve all the face recognition systems. Its usefulness is dependent on the specific methods employed in practice.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith School of Engineering
Science, Environment, Engineering and Technology
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Alqahtani, Faleh Mohammed A. "Three-dimensional facial tracker using a stereo vision system." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/131825/1/Faleh%20Mohammed%20A_Alqahtani_Thesis.pdf.

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This thesis develops an algorithm enabling accurate tracking of human faces, precise estimation of head poses, efficient resolution of occlusions and improved depth perception under different lighting conditions. The system also utilises two stereo cameras that have the ability to track movements across six degrees of freedom, thereby accounting for pose variations. The system can address circumstances in which facial features are no longer discernible, as the results demonstrate increased accuracy in real-time estimation of head poses and facial landmark features. It can also precisely map facial features in different head poses, making it extremely robust for applying 3D facial tracking solutions.
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Landmann, Tobias [Verfasser]. "A case study for Skukuza: Estimating biophysical properties of fires using EOS-MODIS satellite data : A field and remote sensing study to quantify burnt area and fire effects in South African semi-arid savannas / Tobias Landmann." Aachen : Shaker, 2004. http://d-nb.info/1172610657/34.

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Zemánek, Ondřej. "Počítání vozidel v statickém obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417211.

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Tato práce se zaměřuje na problém počítání vozidel v statickém obraze bez znalosti geometrických vlastností scény. V rámci řešení bylo implementováno a natrénováno 5 architektur konvolučních neuronových sítí. Také byl pořízen rozsáhlý dataset s 19 310 snímky pořízených z 12pohledů a zachycujících 7 různých scén. Použité konvoluční sítě mapují vstupní vzorek na mapu hustoty vozidel, ze které lze získat jejich počet a lokalizaci v kontextu vstupního snímku. Hlavním přínosem této práce je porovnání a aplikace dosavadních nejlepších řešení pro počítání objektů v obraze. Většina z těchto architektur byla navržena pro počítání lidí v obraze, proto musely být uzpůsobeny pro potřeby počítání vozidel v statickém obraze. Natrénované modely jsou vyhodnoceny GAME metrikou na TRANCOS datasetu a na velkém spojeném datasetu. Dosažené výsledky všech modelů jsou následně popsány a porovnány.
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Cruz, Daniel Filipe Gonçalves. "Automatic anatomical landmark location estimation in orthopedics." Master's thesis, 2021. http://hdl.handle.net/10316/98097.

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Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia
A Artroplastia total do joelho (ATJ) é um procedimento cirúrgico que consiste na substituição da região articular do joelho por uma prótese do joelho. Foram investigados e desenvolvidos sistemas de navegação baseados em computadores para melhorar o resultados destes procedimento cirúrgicos. Estes sistemas ajudam o cirurgião em planear a posição mais adequada para as próteses e auxiliam-no durante o procedimento cirúrgico a seguir da forma mais eficiente o plano cirúrgico definido.Esta tese é desenvolvida na recente introdução de sistemas de navegação por vídeo para ATJ e é focada em navegação image-free. Este tipo de navegação, requer a aquisição de determinadas pontos de referência anatómicas durante a cirurgia. No entanto, muitas vezes o processo de identificação destes pontos de referência é realizado manualmente, o que é demorado, tem falta de precisão e alta variabilidade, levando a erros significativos no posicionamento das próteses. Este trabalho apresenta uma pipeline para deteção e localização automática dos pontos de referência a partir de imagens RGB capturadas durante a cirurgia. O objetivo é guiar e assistir cirurgiões em localizar os pontos de referência fornecendo sugestões em tempo real sobre a localização dos mesmos. A solução proposta é baseada em algoritmos do estado da arte de deep learning, que combinamos com uma estratégia rápida e eficiente de gerar dados de treino que desenvolvemos especificamente para o nosso problema. Os resultados experimentais obtidos utilizando dados de cirurgias reais revelam um desempenho promissor, apresentando capacidade de generalização para diferentes dados intra-operatórios e estimações confiáveis que atendem aos requisitos clínicos e funciona em tempo real.
Total knee arthroplasty (TKA) is a surgical procedure that consists in replacing the entire knee joint by artificial knee implants. Computer-based navigation systems have been investigated and developed to improve the outcome of TKA procedures. These systems support the surgeon in planning the most adequate position for the implants, and assist during the procedure in effectively following the defined surgical plan. This thesis is built on the recent introduction of video-based navigation systems in the context of TKA and is focused on image-free navigation. This type of navigation requires the acquisition of particular anatomical landmarks intra-operatively. The accurate localization of these anatomical landmarks is essential for the success of the surgery. However, the landmark identification process is often conducted manually, which is time-consuming, lacks accuracy and has high variability, leading to significant errors in implant positioning. This work presents an end-to-end pipeline for automatic detection and localization of anatomical landmarks from RGB images acquired during the surgery. The aim is to guide and assist surgeons in locating the anatomical landmarks by providing real time suggestions about the landmark location. The proposed solution is based in state-of-the-art deep learning strategies, which we combine with a fast and effective labelling framework which we developed specifically to provide the required annotations. The experimental results using real surgery data show encouraging performance, presenting generelization capabilities for unseen data and reliable predictions that meet the clinical requirements, running in real-time.
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Cheng, Yi-Tseng, and 鄭亦曾. "Landmark Oriented Generalized Biologically Inspired Features for Age Estimation." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/75001536503865780052.

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碩士
國立臺灣科技大學
機械工程系
104
We propose the Generalized Biologically Inspired Features (GBIFs) and a moving segmentation scheme followed by soft boundary regression for age estimation. The GBIF is more advantageous than the Bio-Inspired Feature (BIF) for capturing age-related facial traits. The moving segmentation is proposed to better determine the age groups, leading to an improvement on the age estimation accuracy. Different from most approaches that segment the age groups in an ad-hoc way, the moving segmentation allows one to define age groups using the local minima in the misclassification rate across ages. The extraction of the GBIF depends on the partition of component regions defined by facial landmarks. In addition to the partition of component regions, we also study the appropriate age grouping and hierarchical classification, and determine the best configuration for age estimation. The proposed approach with the most appropriate settings outperforms most of the state of the art on two benchmarks, FG-NET and MORPH.
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Honari, Sina. "Feature extraction on faces : from landmark localization to depth estimation." Thèse, 2018. http://hdl.handle.net/1866/22658.

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Karmali, Tejan. "Landmark Estimation and Image Synthesis Guidance using Self-Supervised Networks." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5899.

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The exponential rise in the availability of data over the past decade has fuelled research in deep learning. While supervised deep learning models achieve near-human performance using annotated data, it comes with an additional cost of annotation. Additionally, there could be ambiguity in annotations due to human error. While an image classification task assigns one label to the whole image, as we increase the granularity of the task to landmark estimation, the annotator needs to pinpoint the landmark accurately. The self-supervised learning (SSL) paradigm overcomes these concerns by using pretext task based objectives to learn from large-scale unannotated data. In this work, we show how to extract relevant signals from pretrained self-supervised networks for a) a discriminative task of landmark estimation under limited annotations, and b) increasing perceptual quality of the images generated by generative adversarial network. In this first part, we demonstrate the emergent correspondence tracking properties in the non-contrastive SSL framework. Using this as supervision, we propose LEAD which is an approach to discover landmarks from an unannotated collection of category-specific images. Existing works in self-supervised landmark detection are based on learning dense (pixel-level) feature representations from an image, which are further used to learn landmarks in a semi-supervised manner. While there have been advances in self-supervised learning of image features for instance-level tasks like classification, these methods do not ensure dense equivariant representations. The property of equivariance is of interest for dense prediction tasks like landmark estimation. In this work, we introduce an approach to enhance the learning of dense equivariant representations in a self-supervised fashion. We follow a two-stage training approach: first, we train a network using the BYOL objective which operates at an instance level. The correspondences obtained through this network are further used to train a dense and compact representation of the image using a lightweight network. We show that having such a prior in the feature extractor helps in landmark detection, even under a drastically limited number of annotations while also improving generalization across scale variations. Next, we utilize the rich feature space from the SSL framework as a “naturalness” prior to alleviate unnatural image generation from Generative Adversarial Networks (GAN), which is a popular class of generative models. Progress in GANs has enabled the generation of high-resolution photorealistic images of astonishing quality. StyleGANs allow for compelling attribute modification on such images via mathematical operations on the latent style vectors in the W/W+ space that effectively modulates the rich hierarchical representations of the generator. Such operations have recently been generalized beyond mere attribute swapping in the original StyleGAN paper to include interpolations. In spite of many significant improvements in StyleGANs, they are still seen to generate unnatural images. The quality of the generated images is a function of, (a) richness of the hierarchical representations learned by the generator, and, (b) linearity and smoothness of the style spaces. In this work, we propose Hierarchical Semantic Regularizer (HSR) which aligns the hierarchical representations learnt by the generator to corresponding powerful features learned by pretrained networks on large amounts of data. HSR not only improves generator representations but also the linearity and smoothness of the latent style spaces, leading to the generation of more natural-looking style-edited images. To demonstrate improved linearity, we propose a novel metric - Attribute Linearity Score (ALS). A significant reduction in the generation of unnatural images is corroborated by improvement in the Perceptual Path Length (PPL) metric by 15% across different standard datasets while simultaneously improving the linearity of attribute-change in the attribute editing tasks.
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Books on the topic "Landmark Estimation"

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Ramsay, James. Curve registration. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.9.

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This article deals with curve registration, which refers to methods for aligning prominent features in a set of curves by transforming their abscissa variables. It first illustrates the concepts of amplitude and phase variation schematically and with real data before defining the time-warping functions and their functional inverse. It then describes the decomposition of total mean squared variation into separate amplitude and phase components, along with an R2 measure of the proportion of functional variation due to phase in a sample of curves. It also considers landmark registration, novel ways of defining curve features, continuous registration, and methods based on structured models for amplitude and phase variation combined with more statistically oriented fitting methods such as maximum likelihood or Bayesian estimation. The article concludes with a brief survey of software resources for registration.
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Desai, Anjali, and Andrew S. Epstein. Doctors’ Prognostic Accuracy in Terminally Ill Patients (DRAFT). Edited by Nathan A. Gray and Thomas W. LeBlanc. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190658618.003.0031.

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“Doctors’ Prognostic Accuracy in Terminally Ill Patients” reviews one of Christakis and Lamont’s landmark articles, which investigated the factors associated with prognostic accuracy (and prognostic error) in doctors’ prognoses for terminally ill patients. The article explored the extent and determinants of optimistic errors, pessimistic errors, and correct predictions among doctors who were estimating prognoses for their terminally ill patients. This chapter offers a concise breakdown of the study’s design and salient study results while also pointing out study limitations. The chapter summarizes other relevant studies exploring prognostic estimates and prognostic disclosure by physicians to terminally ill cancer patients. Finally, the chapter provides a clinical case to illustrate some of the study’s practical implications for patient care.
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Book chapters on the topic "Landmark Estimation"

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Lee, Donghoon, Junyoung Chung, and Chang D. Yoo. "Joint Estimation of Pose and Face Landmark." In Computer Vision -- ACCV 2014, 305–19. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16817-3_20.

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Liu, Yin, Ying Cui, and Zhong Jin. "Neighborhood-Preserving Estimation Algorithm for Facial Landmark Points." In Intelligent Science and Intelligent Data Engineering, 630–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36669-7_77.

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Sommer, Stefan, Alexis Arnaudon, Line Kuhnel, and Sarang Joshi. "Bridge Simulation and Metric Estimation on Landmark Manifolds." In Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics, 79–91. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67675-3_8.

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Payer, Christian, Martin Urschler, Horst Bischof, and Darko Štern. "Uncertainty Estimation in Landmark Localization Based on Gaussian Heatmaps." In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis, 42–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60365-6_5.

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Galindo, Ramiro, Wilbert G. Aguilar, and Rolando P. Reyes Ch. "Landmark Based Eye Ratio Estimation for Driver Fatigue Detection." In Intelligent Robotics and Applications, 565–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27541-9_46.

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Wu, Hongbo, Chris Bailey, Parham Rasoulinejad, and Shuo Li. "Automatic Landmark Estimation for Adolescent Idiopathic Scoliosis Assessment Using BoostNet." In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017, 127–35. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66182-7_15.

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Avisdris, Netanell, Leo Joskowicz, Brian Dromey, Anna L. David, Donald M. Peebles, Danail Stoyanov, Dafna Ben Bashat, and Sophia Bano. "BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planes." In Lecture Notes in Computer Science, 279–89. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16440-8_27.

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Yu, Yu, Gang Liu, and Jean-Marc Odobez. "Deep Multitask Gaze Estimation with a Constrained Landmark-Gaze Model." In Lecture Notes in Computer Science, 456–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11012-3_35.

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Sarkar, Rajib, Siddhartha Bhattacharyya, Debashis De, and Asit K. Datta. "Landmark Identification from Low-Resolution Real-Time Image for Pose Estimation." In Lecture Notes in Electrical Engineering, 1–15. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8477-8_1.

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Trusheim, Felix, Alexandru Condurache, and Alfred Mertins. "Visual Landmark Based 3D Road Course Estimation with Black Box Variational Inference." In Computer Analysis of Images and Patterns, 332–43. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64689-3_27.

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Conference papers on the topic "Landmark Estimation"

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Suganya, S., and Narasimhan Ranga Raajan. "Augmented reality-landmark estimation." In 2011 International Conference on Recent Advancements in Electrical, Electronics and Control Engineering (ICONRAEeCE). IEEE, 2011. http://dx.doi.org/10.1109/iconraeece.2011.6129793.

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Burgos-Artizzu, Xavier P., Pietro Perona, and Piotr Dollar. "Robust Face Landmark Estimation under Occlusion." In 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, 2013. http://dx.doi.org/10.1109/iccv.2013.191.

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Farrokhsiar, Morteza, and Homayoun Najjaran. "Rao-Blackwellized Particle Filter Approach to Monocular vSLAM With a Modified Initialization Scheme." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87610.

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This paper presents a Rao-Blackwellized particle filter (RBPF) approach with a modified undelayed initialization scheme to solve the 3D visual SLAM problem (vSLAM) using a single camera. In the proposed method, landmarks are initialized using the inverse depth of the landmarks rather than the traditional use of their depths. In this scheme, there is no need to distinguish between partially and fully initialized landmarks. Once the landmarks are properly initialized, the RBPF enhances the estimation of the robot path and landmark location using bearing-only information obtained from a camera. The results of numerical simulations and experiments with a video clip have been included in this paper to verify the performance of the proposed approach.
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Liu, Hong, Meijia Song, Wei Shi, and Xia Li. "Position Constraint Loss For Fashion Landmark Estimation." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054508.

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Wen, Zhihua, and Michael Rabinovich. "Network distance estimation with dynamic landmark triangles." In the 2008 ACM SIGMETRICS international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1375457.1375507.

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Lim, Jihu, Dohun Kim, Sanghyun Park, and Joonki Paik. "Face Landmark Estimation-based De-identification System." In 2022 International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 2022. http://dx.doi.org/10.1109/iceic54506.2022.9748390.

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Civir, Cevdet, and Cihan Topal. "Robust Landmark Selection for 3D Face Pose Estimation." In 2019 27th Signal Processing and Communications Applications Conference (SIU). IEEE, 2019. http://dx.doi.org/10.1109/siu.2019.8806384.

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Li, Ming, Zhonghua Liu, Jianan Huang, and Kenji Imou. "Landmark direction angle estimation based on omnidirectional image." In 2010 International Conference on Information and Automation (ICIA). IEEE, 2010. http://dx.doi.org/10.1109/icinfa.2010.5512316.

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Chin, Yi, and Chun-Jen Tsai. "Bayesian dense motion field estimation with landmark constraint." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5652489.

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Werner, Philipp, Frerk Saxen, and Ayoub Al-Hamadi. "Landmark based head pose estimation benchmark and method." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8297015.

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