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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 192 (0 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 172 (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...
A survey of shape similarity assessment algorithms for product design and manufacturing applications
 Journal of Computing and Information Science in Engineering
, 2003
"... This document contains the draft version of the following paper: A. Cardone, S.K. Gupta, and M. Karnik. A survey of shape similarity assessment algorithms for product design and manufacturing applications. ASME Journal of ..."
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Cited by 45 (13 self)
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This document contains the draft version of the following paper: A. Cardone, S.K. Gupta, and M. Karnik. A survey of shape similarity assessment algorithms for product design and manufacturing applications. ASME Journal of
Algorithmic Modeling for Performance Evaluation
 Machine Vision Applications
, 1997
"... Introduction 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 rare ..."
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Cited by 19 (6 self)
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Introduction 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 proportion of failure cases that allows the reliability to be assessed and a large number of failure cases are needed to form an accurate estimation of reliability. For reliable and robust algorithms, this requires an inordinate number of test cases. Secondly, the difficulty of selecting test images to ensure that they are representative. This is aggravated by fact that assumptions made may be valid in one application domain but not in another. This makes it very difficult to relate the results of one evaluation to othe
Performance characterisation in computer vision: The role of statistics in testing and design
 Imaging and Vision Systems: Theory, Assessment and Applications. NOVA Science Books
, 1993
"... We consider the relationship between the performance characteristics of vision algorithms and algorithm design. In the first part we discuss the issues involved in testing. A description of good practice is given covering test objectives, test data, test metrics and the test protocol. In the second ..."
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Cited by 16 (5 self)
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We consider the relationship between the performance characteristics of vision algorithms and algorithm design. In the first part we discuss the issues involved in testing. A description of good practice is given covering test objectives, test data, test metrics and the test protocol. In the second part we discuss aspects of good algorithmic design including understanding of the statistical properties of data and common algorithmic operations, and suggest how some common problems may be overcome. 1
Line pattern retrieval using relational histograms
 IEEE TRANS. PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1999
"... This paper presents a new compact shape representation for retrieving linepatterns from large databases. The basic idea is to exploit both geometric attributes and structural information to construct a shape histogram. We realize this goal by computing the Nnearest neighbor graph for the linesse ..."
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Cited by 16 (4 self)
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This paper presents a new compact shape representation for retrieving linepatterns from large databases. The basic idea is to exploit both geometric attributes and structural information to construct a shape histogram. We realize this goal by computing the Nnearest neighbor graph for the linessegments for each pattern. The edges of the neighborhood graphs are used to gate contributions to a twodimensional pairwise geometric histogram. Shapes are indexed by searching for the linepattern that maximizes the cross correlation of the normalized histogram bincontents. We evaluate the new method on a database containing over 2,500 linepatterns each composed of hundreds of lines.
Performance characterization in computer vision: A guide to best practices
, 2007
"... It is frequently remarked that designers of computer vision algorithms and systems cannot reliably predict how algorithms will respond to new problems. A variety of reasons have been given for this situation and a variety of remedies prescribed in literature. Most of these involve, in some way, payi ..."
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Cited by 8 (0 self)
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It is frequently remarked that designers of computer vision algorithms and systems cannot reliably predict how algorithms will respond to new problems. A variety of reasons have been given for this situation and a variety of remedies prescribed in literature. Most of these involve, in some way, paying greater attention to the domain of the problem and to performing detailed empirical analysis. The goal of this paper is to review what we see as current best practices in these areas and also suggest refinements that may benefit the field of computer vision. A distinction is made between the historical emphasis on algorithmic novelty and the increasing importance of validation on particular data sets and problems.
Inexact Graph Retrieval
, 1999
"... This paper describes a graphmatching technique for recognising linepattern shapes in large image databases. We use a Bayesian matching algorithm that draws on edgeconsistency and node attribute similarity. This information is used to determine the a posteriori probability of a query graph for eac ..."
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Cited by 5 (0 self)
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This paper describes a graphmatching technique for recognising linepattern shapes in large image databases. We use a Bayesian matching algorithm that draws on edgeconsistency and node attribute similarity. This information is used to determine the a posteriori probability of a query graph for each of the candidate matches in the database. The node featurevectors are constructed by computing normalised histograms of pairwise geometric attributes. Attribute similarity is assessedbycomputing the Bhattacharyya distance between the histograms. Recognition is realised by selecting the candidate from the database which has the largestaposteriori probability. 1 Introduction Broadly speaking there are two sources of information that can be tapped in the content based retrieval of images from large databases. The first of these is to use a compact summary of the image attributes. One of the best known examples here is the attribute histogram originally popularised bySwain and Ballard [1]...
BFitting: An Estimation Technique with Automatic Parameter Selection
 In British Machine Vision Conference
, 1996
"... The problem of model selection is endemic in the machine vision literature, yet largely unsolved in the statistical field. Our recent work on a theoretical statistical evaluation of the Bhattacharyya similarity metric has led us to conclude that this measure can be used to provide a solution. He ..."
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Cited by 3 (1 self)
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The problem of model selection is endemic in the machine vision literature, yet largely unsolved in the statistical field. Our recent work on a theoretical statistical evaluation of the Bhattacharyya similarity metric has led us to conclude that this measure can be used to provide a solution. Here we describe how the approach may be extended to solve the problem of model selection during the functional fitting process. This paper outlines the motivation for this work and a preliminary study of the use of the technique for function fitting. It is shown how the application of the method to polynomial interpolation provides some interesting insights into the behaviour of these statistical methods and suggestions are given for possible uses of this technique in vision. 1 System Identification In some areas of machine vision the correct model required to describe a system can be derived without any real ambiguity. These situations are exemplified by the process of camera calibra...
Using Quantitative Statistics for the Construction of Machine Vision Systems.
, 2003
"... This paper describes a design methodology for constructing machine vision systems. Central to this is the use of empirical design techniques and in particular quantitative statistics. The approach views both the construction and evaluation of systems as one and is based upon what could be regarded a ..."
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Cited by 2 (1 self)
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This paper describes a design methodology for constructing machine vision systems. Central to this is the use of empirical design techniques and in particular quantitative statistics. The approach views both the construction and evaluation of systems as one and is based upon what could be regarded as a set of selfevident propositions; • Vision algorithms must deliver information allowing practical decisions regarding interpretation of an image. • Probability is the only selfconsistent computational framework for data analysis, and so must form the basis of all algorithmic analysis processes. • The most effective and robust algorithms will be those that match most closely the statistical properties of the data. • A statistically based algorithm which takes correct account of all available data will yield an optimal result. 1. Machine vision research has not emphasised the need for (or necessary methods of) algorithm characterisation, which is unfortunate, as the subject cannot advance without a sound empirical base. In general this problem can be attributed to one of two factors; a poor understanding of the role of assumptions and statistics, and a lack of appreciation of what is to be done with the generated data. The methodology described here focuses on identifying the statistical characteristics of the data and matching these to the assuptions of the underlying techniques. The methodology has been developed from more than a decade of vision design and testing, which has culminated in the construction of the TINA open source image analysis / machine vision system [htt://www.tinavision.net]. 1