Дисертації з теми "Object recognition from optical images"
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1
Illing, Diane Patricia. "Orientation and recognition of both noisy and partially occluded 3-D objects from single 2-D images." Thesis, University of South Wales, 1990. https://pure.southwales.ac.uk/en/studentthesis/orientation-and-recognition-of-both-noisy-and-partially-occluded-3d-objects-from-single-2d-images(c849d6e3-24e4-4462-9afb-c608120a4019).html.
Повний текст джерелаАнотація:
This work is concerned with the problem of 3-D object recognition and orientation determination from single 2-D image frames in which objects may be noisy, partially occluded or both. Global descriptors of shape such as moments and Fourier descriptors rely on the whole shape being present. If part of a shape is missing then all of the descriptors will be affected. Consequently, such approaches are not suitable when objects are partially occluded, as results presented here show. Local methods of describing shape, where distortion of part of the object affects only the descriptors associated with that particular region, and nowhere else, are more likely to provide a successful solution to the problem. One such method is to locate points of maximum curvature on object boundaries. These are commonly believed to be the most perceptually significant points on digital curves. However, results presented in this thesis will show that estimators of point curvature become highly unreliable in the presence of noise. Rather than attempting to locate such high curvature points directly, an approach is presented which searches for boundary segments which exhibit significant linearity; curvature discontinuities are then assigned to the junctions between boundary segments. The resulting object descriptions are more stable in the presence of noise. Object orientation and recognition is achieved through a directed search and comparison to a database of similar 2-D model descriptions stored at various object orientations. Each comparison of sensed and model data is realised through a 2-D pose-clustering procedure, solving for the coordinate transformation which maps model features onto image features. Object features are used both to control the amount of computation and to direct the search of the database. In conditions of noise and occlusion objects can be recognised and their orientation determined to within less than 7 degrees of arc, on average.
2
Izciler, Fatih. "3d Object Recognition From Range Images." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614915/index.pdf.
Повний текст джерелаАнотація:
Recognizing generic objects by single or multi view range images is a contemporary popular problem in 3D object recognition area with developing technology of scanning devices such as laser range scanners. This problem is vital to current and future vision systems performing shape based matching and classification of the objects in an arbitrary scene. Despite improvements on scanners, there are still imperfections on range scans such as holes or unconnected parts on images. This studyobjects at proposing and comparing algorithms that match a range image to complete 3D models in a target database.The study started with a baseline algorithm which usesstatistical representation of 3D shapesbased on 4D geometricfeatures, namely SURFLET-Pair relations.The feature describes the geometrical relationof a surface-point pair and reflects local and the global characteristics of the object. With the desire of generating solution to the problem,another algorithmthat interpretsSURFLET-Pairslike in the baseline algorithm, in which histograms of the features are used,isconsidered. Moreover, two other methods are proposed by applying 2D space filing curves on range images and applying 4D space filling curves on histograms of SURFLET-Pairs. Wavelet transforms are used for filtering purposes in these algorithms. These methods are tried to be compact, robust, independent on a global coordinate frame and descriptive enough to be distinguish queries&rsquocategories.Baseline and proposed algorithms are implemented on a database in which range scans of real objects with imperfections are queries while generic 3D objects from various different categories are target dataset.
3
Hong, Tao. "Object recognition with features from complex wavelets." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610239.
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4
Gadsby, David. "Object recognition for threat detection from 2D X-ray images." Thesis, Manchester Metropolitan University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493851.
Повний текст джерелаАнотація:
This thesis examines methods to identify threat objects inside airport handheld passenger baggage. The work presents techniques for the enhancement and classification of objects from 2-dimensional x-ray images. It has been conducted with the collaboration of Manchester Aviation Services and uses test images from real x-ray baggage machines. The research attempts to overcome the key problem of object occlusion that impedes the performance of x-ray baggage operators identifying threat objects such as guns and knifes in x-ray images. Object occlusions can hide key information on the appearance of an object and potentially lead to a threat item entering an aircraft.
5
Villalobos, Leda. "Three dimensional primitive CAD-based object recognition from range images." Case Western Reserve University School of Graduate Studies / OhioLINK, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=case1057759966.
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6
Малишевська, Катерина Миколаївна. "Інтелектуальна система для розпізнавання об'єктів на оптичних зображеннях з використанням каскадних нейронних мереж". Doctoral thesis, Київ, 2015. https://ela.kpi.ua/handle/123456789/14391.
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7
Schlecht, Joseph. "Learning 3-D Models of Object Structure from Images." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/194661.
Повний текст джерелаАнотація:
Recognizing objects in images is an effortless task for most people.Automating this task with computers, however, presents a difficult challengeattributable to large variations in object appearance, shape, and pose. The problemis further compounded by ambiguity from projecting 3-D objects into a 2-D image.In this thesis we present an approach to resolve these issues by modeling objectstructure with a collection of connected 3-D geometric primitives and a separatemodel for the camera. From sets of images we simultaneously learn a generative,statistical model for the object representation and parameters of the imagingsystem. By learning 3-D structure models we are going beyond recognitiontowards quantifying object shape and understanding its variation.We explore our approach in the context of microscopic images of biologicalstructure and single view images of man-made objects composed of block-likeparts, such as furniture. We express detected features from both domains asstatistically generated by an image likelihood conditioned on models for theobject structure and imaging system. Our representation of biological structurefocuses on Alternaria, a genus of fungus comprising ellipsoid and cylindershaped substructures. In the case of man-made furniture objects, we representstructure with spatially contiguous assemblages of blocks arbitrarilyconstructed according to a small set of design constraints.We learn the models with Bayesian statistical inference over structure andcamera parameters per image, and for man-made objects, across categories, suchas chairs. We develop a reversible-jump MCMC sampling algorithm to exploretopology hypotheses, and a hybrid of Metropolis-Hastings and stochastic dynamicsto search within topologies. Our results demonstrate that we can infer both 3-Dobject and camera parameters simultaneously from images, and that doing soimproves understanding of structure in images. We further show how 3-D structuremodels can be inferred from single view images, and that learned categoryparameters capture structure variation that is useful for recognition.
8
Zhang, Shujun. "Model-based 3D object perception from single monochromatic images of unknown environments." Thesis, University of Reading, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315501.
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9
Ziqing, Li S. "Towards 3D vision from range images : an optimisation framework and parallel distributed networks." Thesis, University of Surrey, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.291880.
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10
Kanaparthi, Pradeep Kumar. "Detection and Recognition of U.S. Speed Signs from Grayscale Images for Intelligent Vehicles." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1352934398.
Повний текст джерела
11
HE, LEI. "A COMPARISON OF DEFORMABLE CONTOUR METHODS AND MODEL BASED APPROACH USING SKELETON FOR SHAPE RECOVERY FROM IMAGES." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1059746287.
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12
"Parameter optimization and learning for 3D object reconstruction from line drawings." 2010. http://library.cuhk.edu.hk/record=b5894303.
Повний текст джерелаАнотація:
Du, Hao.Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.
Includes bibliographical references (p. 61).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- 3D Reconstruction from 2D Line Drawings and its Applications --- p.1
Chapter 1.2 --- Algorithmic Development of 3D Reconstruction from 2D Line Drawings --- p.3
Chapter 1.2.1 --- Line Labeling and Realization Problem --- p.4
Chapter 1.2.2 --- 3D Reconstruction from Multiple Line Drawings --- p.5
Chapter 1.2.3 --- 3D Reconstruction from a Single Line Drawing --- p.6
Chapter 1.3 --- Research Problems and Our Contributions --- p.12
Chapter 2 --- Adaptive Parameter Setting --- p.15
Chapter 2.1 --- Regularities in Optimization-Based 3D Reconstruction --- p.15
Chapter 2.1.1 --- Face Planarity --- p.18
Chapter 2.1.2 --- Line Parallelism --- p.19
Chapter 2.1.3 --- Line Verticality --- p.19
Chapter 2.1.4 --- Isometry --- p.19
Chapter 2.1.5 --- Corner Orthogonality --- p.20
Chapter 2.1.6 --- Skewed Facial Orthogonality --- p.21
Chapter 2.1.7 --- Skewed Facial Symmetry --- p.22
Chapter 2.1.8 --- Line Orthogonality --- p.24
Chapter 2.1.9 --- Minimum Standard Deviation of Angles --- p.24
Chapter 2.1.10 --- Face Perpendicularity --- p.24
Chapter 2.1.11 --- Line Collinearity --- p.25
Chapter 2.1.12 --- Whole Symmetry --- p.25
Chapter 2.2 --- Adaptive Parameter Setting in the Objective Function --- p.26
Chapter 2.2.1 --- Hill-Climbing Optimization Technique --- p.28
Chapter 2.2.2 --- Adaptive Weight Setting and its Explanations --- p.29
Chapter 3 --- Parameter Learning --- p.33
Chapter 3.1 --- Construction of A Large 3D Object Database --- p.33
Chapter 3.2 --- Training Dataset Generation --- p.34
Chapter 3.3 --- Parameter Learning Framework --- p.37
Chapter 3.3.1 --- Evolutionary Algorithms --- p.38
Chapter 3.3.2 --- Reconstruction Error Calculation --- p.39
Chapter 3.3.3 --- Parameter Learning Algorithm --- p.41
Chapter 4 --- Experimental Results --- p.45
Chapter 4.1 --- Adaptive Parameter Setting --- p.45
Chapter 4.1.1 --- Use Manually-Set Weights --- p.45
Chapter 4.1.2 --- Learn the Best Weights with Different Strategies --- p.48
Chapter 4.2 --- Evolutionary-Algorithm-Based Parameter Learning --- p.49
Chapter 5 --- Conclusions and Future Work --- p.53
Bibliography --- p.55
13
師樂善. "A Study on Underwater Object Recognition--Application of Optical Images and Acoustic Range Data." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/50934098777639864823.
Повний текст джерелаАнотація:
碩士國立臺灣大學
造船及海洋工程學研究所
84
The objective of this thesis is to set up a vision model for underwater vehicles; this is, to establish a recognition model for underwater objects by means of optic images and range images which are obtained by the optic image equipment--a video camera and light sources and the simulated acoustic distance measurement equipment--a scanning SONAR in underwater vehicles. Under water, light, absorbed and scattered, reduces its power and contrast; therefore, the viewing range and the quality of underwater images also decline. In this condition, the image contrast can be improved by means of the geometric position of the light source and the camera. If a perfect 3-D range image can be gained, it is possible to calculate the geometric position of this 3-D object and to recognize this object. On the other hand, acoustic range images are obtained through pulse-echo time of flight, which are gained through the acoustic time of flight in media. Acoustic is likely to be affected by some factors such as the quality of the reflection surface; consequently, range data are the image resulting from strong reflection signals. Therefore, the emphasis of this thesis is to utilize both optic images and range images as the vision model for automatic underwater vehicles(AUV). This thesis has finished common optic image processing, edge-detecting, range image simulation, range image coordinate transformation, given object recognition model. The final stage is to utilize Hough transform to obtain the edge characteristics, and then to describe the distinctive features so as to establish recognition rules. The last step is to take advantage of expert system as an assistant tool to recognize the given underwater objects.
14
Li, Patrick. ""Flobject" Analysis: Learning about Static Images from Motion." Thesis, 2011. http://hdl.handle.net/1807/31310.
Повний текст джерелаАнотація:
A critical practical problem in the field of object recognition is an insufficient number of labeled training images, as manually labeling images is a time consuming task. For this reason, unsupervised learning techniques are used to take advantage of unlabeled training images to extract image representations that are useful for classification. However, unsupervised learning is in general difficult. We propose simplifying the unsupervised training problem considerably by taking the advance of motion information. The output of our method is a model that can generate a vector representation from any static image. However, the model is trained using images with additional motion information. To demonstrate the flobject analysis framework, we extend the latent Dirichlet allocation model to account for word-specific flow vectors. We show that the static image representations extracted using our model achieve higher classification rates and better generalization than standard topic models, spatial pyramid matching, and Gist descriptors.
15
Pillay, Maldean. "Gabor filter parameter optimization for multi-textured images : a case study on water body extraction from satellite imagery." Thesis, 2012. http://hdl.handle.net/10413/11070.
Повний текст джерелаАнотація:
The analysis and identification of texture is a key area in image processing and computer
vision. One of the most prominent texture analysis algorithms is the Gabor Filter.
These filters are used by convolving an image with a family of self similar filters or
wavelets through the selection of a suitable number of scales and orientations, which
are responsible for aiding in the identification of textures of differing coarseness and
directions respectively.
While extensively used in a variety of applications, including, biometrics such as iris and
facial recognition, their effectiveness depend largely on the manual selection of different
parameters values, i.e. the centre frequency, the number of scales and orientations, and
the standard deviations. Previous studies have been conducted on how to determine
optimal values. However the results are sometimes inconsistent and even contradictory.
Furthermore, the selection of the mask size and tile size used in the convolution process
has received little attention, presumably since they are image set dependent.
This research attempts to verify specific claims made in previous studies about the
influence of the number of scales and orientations, but also to investigate the variation of
the filter mask size and tile size for water body extraction from satellite imagery. Optical
satellite imagery may contain texture samples that are conceptually the same (belong
to the same class), but are structurally different or differ due to changes in illumination,
i.e. a texture may appear completely different when the intensity or position of a light
source changes.
A systematic testing of the effects of varying the parameter values on optical satellite
imagery is conducted. Experiments are designed to verify claims made about the influence of varying the scales and orientations within predetermined ranges, but also to
show the considerable changes in classification accuracy when varying the filter mask
and tile size. Heuristic techniques such as Genetic Algorithms (GA) can be used to find
optimum solutions in application domains where an enumeration approach is not feasible.
Hence, the effectiveness of a GA to automate the process of determining optimum
Gabor filter parameter values for a given image dataset is also investigated.
The results of the research can be used to facilitate the selection of Gabor filter parameters
for applications that involve multi-textured image segmentation or classification,
and specifically to guide the selection of appropriate filter mask and tile sizes for automated
analysis of satellite imagery.Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2012.