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Shape Distributions
 ACM Transactions on Graphics
, 2002
"... this paper, we propose and analyze a method for computing shape signatures for arbitrary (possibly degenerate) 3D polygonal models. The key idea is to represent the signature of an object as a shape distribution sampled from a shape function measuring global geometric properties of an object. The pr ..."
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Cited by 279 (2 self)
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this paper, we propose and analyze a method for computing shape signatures for arbitrary (possibly degenerate) 3D polygonal models. The key idea is to represent the signature of an object as a shape distribution sampled from a shape function measuring global geometric properties of an object. The primary motivation for this approach is to reduce the shape matching problem to the comparison of probability distributions, which is simpler than traditional shape matching methods that require pose registration, feature correspondence, or model fitting
Matching 3D Models with Shape Distributions
"... Measuring the similarity between 3D shapes is a fundamental problem, with applications in computer vision, molecular biology, computer graphics, and a variety of other fields. A challenging aspect of this problem is to find a suitable shape signature that can be constructed and compared quickly, whi ..."
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Cited by 212 (7 self)
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Measuring the similarity between 3D shapes is a fundamental problem, with applications in computer vision, molecular biology, computer graphics, and a variety of other fields. A challenging aspect of this problem is to find a suitable shape signature that can be constructed and compared quickly, while still discriminating between similar and dissimilar shapes. In this paper, we propose and analyze a method for computing shape signatures for arbitrary (possibly degenerate) 3D polygonal models. The key idea is to represent the signature of an object as a shape distribution sampled from a shape function measuring global geometric properties of an object. The primary motivation for this approach is to reduce the shape matching problem to the comparison of probability distributions, which is a simpler problem than the comparison of 3D surfaces by traditional shape matching methods that require pose registration, feature correspondence, or model fitting. We find that the dissimilarities be...
Attribute Trees as Adaptive Object Models in Image Analysis
"... This thesis focuses on the analysis of irregular hierarchical visual objects. The main approach involves modelling the objects as unordered attribute trees. A tree presents the overall organization, or topology, of an object, while the vertex attributes describe further visual properties such as int ..."
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Cited by 4 (0 self)
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This thesis focuses on the analysis of irregular hierarchical visual objects. The main approach involves modelling the objects as unordered attribute trees. A tree presents the overall organization, or topology, of an object, while the vertex attributes describe further visual properties such as intensity, color, or size. Techniques for extracting, matching, comparing, and interpolating attribute trees are presented, principally aiming at systems that can learn to recognize objects. Analysis of weather radar images has been the pilot application for this study, but the main ideas are applicable in structural pattern recognition more generally.
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"... Clinically, the assessment of human cerebral cortical thickness (the thickness of the cortical grey matter ribbon) has massive importance in the determination of pathology and in assessing the processes of “normal ” brain maturation and ageing. ..."
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Clinically, the assessment of human cerebral cortical thickness (the thickness of the cortical grey matter ribbon) has massive importance in the determination of pathology and in assessing the processes of “normal ” brain maturation and ageing.
Now including comments from prominent researchers in the field of computer and machine vision. An Empirical Design Methodology for the Construction of Machine Vision Systems.
"... This document presents a design methodology the aim of which is to provide a framework for constructing machine vision systems. Central to this approach is the use of empirical design techniques and in particular quantitative statistics. The methodology described herein underpins the development of ..."
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This document presents a design methodology the aim of which is to provide a framework for constructing machine vision systems. Central to this approach is the use of empirical design techniques and in particular quantitative statistics. The methodology described herein underpins the development of the TINA [26] open source image analysis environment which in turn provides practical instantiations of the ideas presented. The appendices form the larger part of this document, providing mathematical explanations of the techniques which are regarded as of importance. A summary of these appendices is given below; Appendix A B C D E F G H I J K L
Comparison of Combined Shape Descriptors for Irregular Objects
 In Proceedings of the Eight British Machine Vision Conference (Vol
, 1997
"... This paper focuses on recognition powers and computational efforts of three different shape coding techniques, namely the chain code histogram (CCH), the pairwise geometric histogram (PGH), and the combination of simple shape descriptors, for characterization of irregular objects. In recognizing ..."
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This paper focuses on recognition powers and computational efforts of three different shape coding techniques, namely the chain code histogram (CCH), the pairwise geometric histogram (PGH), and the combination of simple shape descriptors, for characterization of irregular objects. In recognizing irregular objects the essential task is to design efficient measures based on relatively small prior knowledge on geometrical constraints of possible target objects. Three rather different approaches are evaluated and discussed by the means of the selforganizing map (SOM). A database retrieval problem is also assumed to further test their discriminatory powers. As a case study, natural irregular objects have been used. Grouping of these objects based on their visual similarity is the main topic in this paper. The combination of simple shape descriptors is shown to have good recognition capabilities and low computation costs.
Internal. Robust Recognition of Scaled Shapes Using Pairwise Geometric Histograms.
"... The recognition of shapes in images using Pairwise Geometric Histograms has previously been conned to xed scale shape. Although the geometric representation used in this algorithm is not scale invariant, the stable behaviour of the similarity metric as shapes are scaled enables the method to be exte ..."
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The recognition of shapes in images using Pairwise Geometric Histograms has previously been conned to xed scale shape. Although the geometric representation used in this algorithm is not scale invariant, the stable behaviour of the similarity metric as shapes are scaled enables the method to be extended to the recognition of shapes over a range of scale. In this paper the necessary additions to the existing algorithm are described and the technique is demonstrated on real image data. Hypotheses generated by matching scene shape data to models have previously been resolved using the generalised Hough transform. The robustness of this method can be attributed to its approximation of maximum likelihood statistics. To further improve the robustness of the recognition algorithm and to improve the accuracy to which an objects location, orientation and scale can be determined the generalised Hough transform has been replaced by the probabilistic Hough transform. 2
Voronoi seeded colour image segmentation
, 1999
"... The goal of the segmentation scheme presented is to combine edge and region information to achieve a stable segmentation. The segmentation scheme presented is designed to operate on general home and stock photographs, it returns a comprehensive regionbased description of the visual content of an im ..."
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The goal of the segmentation scheme presented is to combine edge and region information to achieve a stable segmentation. The segmentation scheme presented is designed to operate on general home and stock photographs, it returns a comprehensive regionbased description of the visual content of an image (including a distinction between smooth and textured regions and a description of the internal properties of the later). A colour edge detector is presented, where hue difference is weighted moreheavily than brightness difference. Seed points for region growing are derived from the colour edge image as the peaks in the associated Voronoi image. Regions are grown using gates on pixel colour relative to region central colour and region edge pixel colour. This permits regions to encompass shading gradients. Image edges act as hard barriers during region growing. A discrete feature based texture model is derived and then used to unify groups of smaller regions into extended textured regions. The segmentation scheme is designed to facilitate image retrieval and has been tested on a corpus of over 40000 images and has been found to be robust.
Robust Recognition of Scaled Shapes using Pairwise Geometric Histograms
"... The recognition of shapes in images using Pairwise Geometric Histograms has previously been confined to fixed scale shape. Although the geometric representation used in this algorithm is not scale invariant, the stable behaviour of the similarity metric as shapes are scaled enables the method to be ..."
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The recognition of shapes in images using Pairwise Geometric Histograms has previously been confined to fixed scale shape. Although the geometric representation used in this algorithm is not scale invariant, the stable behaviour of the similarity metric as shapes are scaled enables the method to be extended to the recognition of shapes over a range of scale. In this paper the necessary additions to the existing algorithm are described and the technique is demonstrated on real image data. Hypotheses generated by matching scene shape data to models have previously been resolved using the generalised Hough transform. The robustness of this method can be attributed to its approximation of maximum likelihood statistics. To further improve the robustness of the recognition algorithm and to improve the accuracy to which an objects location, orientation and scale can be determined the generalised Hough transform has been replaced by the probabilistic Hough transform. 1
N.A.Thacker, P.Courtney and A.Clark. Last updated
"... Many of the vision algorithms described in the literature are tested on a very small number of images. It is generally agreed that algorithms need to be tested on much larger numbers if any statistically meaningful measure of performance is to be obtained. However, these tests are rarely performed; ..."
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Many of the vision algorithms described in the literature are tested on a very small number of images. It is generally agreed that algorithms need to be tested on much larger numbers if any statistically meaningful measure of performance is to be obtained. However, these tests are rarely performed; in our opinion this is normally due to two reasons. Firstly, the scale of the testing problem when high levels of reliability are sought, since it is the