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Computational Morphology

Author: G.T. Toussaint
Publisher: Elsevier
ISBN: 1483296725
Size: 79.45 MB
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Computational Geometry is a new discipline of computer science that deals with the design and analysis of algorithms for solving geometric problems. There are many areas of study in different disciplines which, while being of a geometric nature, have as their main component the extraction of a description of the shape or form of the input data. This notion is more imprecise and subjective than pure geometry. Such fields include cluster analysis in statistics, computer vision and pattern recognition, and the measurement of form and form-change in such areas as stereology and developmental biology. This volume is concerned with a new approach to the study of shape and form in these areas. Computational morphology is thus concerned with the treatment of morphology from the computational geometry point of view. This point of view is more formal, elegant, procedure-oriented, and clear than many previous approaches to the problem and often yields algorithms that are easier to program and have lower complexity.

Visual Form

Author: C. Arcelli
Publisher: Springer Science & Business Media
ISBN: 1489907157
Size: 21.79 MB
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This book contains the papers presented at the International Workshop on Visual Fonn, held in Capri (Italy) on May 27-30, 1991. The workshop, sponsored by the International Association for Pattern Recognition (!APR), has been jointly organized by the Dipartimento di Infonnatica e Sisternistica of the University of Naples and the Istituto di Cibemetica of the National Research Council of Italy, and has focussed on Shape. Shape is a distinctive feature of most patterns, so that recognition can often be attained through shape discrimination. The organizers of the workshop shared the general feeling manifested by researchers, that it was time for holding a meeting exclusively devoted to a feature so crucial for both human and machine perception. During this meeting, problems and prospects in the field of 2D and 3D shape analysis could be discussed extensively, so as to provide an effective, updated picture of the current research activity in which shape plays a central role. Indeed, many highly qualified researchers in the field positively reacted to the Call for Papers.

Report Cs R

Author: Centrum voor Wiskunde en Informatica (Amsterdam, Netherlands) Dept. of Computer Science
Publisher:
ISBN:
Size: 40.61 MB
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Graph Based Representations In Pattern Recognition

Author: Luc Brun
Publisher: Springer
ISBN: 3540319883
Size: 46.86 MB
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Many vision problems have to deal with di?erent entities (regions, lines, line junctions, etc.) and their relationships. These entities together with their re- tionships may be encoded using graphs or hypergraphs. The structural inf- mation encoded by graphs allows computer vision algorithms to address both the features of the di?erent entities and the structural or topological relati- ships between them. Moreover, turning a computer vision problem into a graph problem allows one to access the full arsenal of graph algorithms developed in computer science. The Technical Committee (TC15, http://www.iapr.org/tcs.html) of the IAPR (International Association for Pattern Recognition) has been funded in order to federate and to encourage research work in these ?elds. Among its - tivities, TC15 encourages the organization of special graph sessions at many computer vision conferences and organizes the biennial workshop GbR. While being designed within a speci?c framework, the graph algorithms developed for computer vision and pattern recognition tasks often share constraints and goals with those developed in other research ?elds such as data mining, robotics and discrete geometry. The TC15 community is thus not closed in its research ?elds but on the contrary is open to interchanges with other groups/communities.

Finding Color And Shape Patterns In Images

Author: Scott Cohen
Publisher:
ISBN:
Size: 40.29 MB
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Abstract: "This thesis is devoted to the Earth Mover's Distance (EMD), an edit distance between distributions, and its use within content-based image retrieval (CBIR). The major CBIR problem discussed is the pattern problem: Given an image and a query pattern, determine if the image contains a region which is visually similar to the pattern; if so, find at least one such image region. An important problem that arises in applying the EMD to CBIR is the EMD under transformation (EMDG̲) problem: find a transformation of one distribution which minimizes its EMD to another, where the set of allowable transformations G is given. The problem of estimating the size/scale at which a pattern occurs in an image is phrased and efficiently solved as an EMDG̲ problem. For a large class of transformation sets, we also present a monotonically convergent iteration to find at least a locally optimal transformation. Our pattern problem solution is the SEDL (Scale Estimation for Directed Location) image retrieval system. Three important contributions of SEDL are (1) a general framework for finding both color and shape patterns, (2) the previously mentioned scale estimation algorithm using the EMD, and (3) a directed (as opposed to exhaustive) search strategy."

A Computational Framework For Segmentation And Grouping

Author: G. Medioni
Publisher: Elsevier
ISBN: 9780080529486
Size: 79.75 MB
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This book represents a summary of the research we have been conducting since the early 1990s, and describes a conceptual framework which addresses some current shortcomings, and proposes a unified approach for a broad class of problems. While the framework is defined, our research continues, and some of the elements presented here will no doubt evolve in the coming years.It is organized in eight chapters. In the Introduction chapter, we present the definition of the problems, and give an overview of the proposed approach and its implementation. In particular, we illustrate the limitations of the 2.5D sketch, and motivate the use of a representation in terms of layers instead. In chapter 2, we review some of the relevant research in the literature. The discussion focuses on general computational approaches for early vision, and individual methods are only cited as references. Chapter 3 is the fundamental chapter, as it presents the elements of our salient feature inference engine, and their interaction. It introduced tensors as a way to represent information, tensor fields as a way to encode both constraints and results, and tensor voting as the communication scheme. Chapter 4 describes the feature extraction steps, given the computations performed by the engine described earlier. In chapter 5, we apply the generic framework to the inference of regions, curves, and junctions in 2-D. The input may take the form of 2-D points, with or without orientation. We illustrate the approach on a number of examples, both basic and advanced. In chapter 6, we apply the framework to the inference of surfaces, curves and junctions in 3-D. Here, the input consists of a set of 3-D points, with or without as associated normal or tangent direction. We show a number of illustrative examples, and also point to some applications of the approach. In chapter 7, we use our framework to tackle 3 early vision problems, shape from shading, stereo matching, and optical flow computation. In chapter 8, we conclude this book with a few remarks, and discuss future research directions. We include 3 appendices, one on Tensor Calculus, one dealing with proofs and details of the Feature Extraction process, and one dealing with the companion software packages.

Computer Vision

Author: Richard Szeliski
Publisher: Springer
ISBN: 9781848829466
Size: 57.23 MB
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Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

Neural Networks For Pattern Recognition

Author: Christopher M. Bishop
Publisher: Oxford University Press
ISBN: 0198538642
Size: 24.40 MB
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`Readers will emerge with a rigorous statistical grounding in the theory of how to construct and train neural networks in pattern recognition' New Scientist