2D object detection and recognition : models, algorithms, and networks / Yali Amit.
By: Amit, Yali [author.].
Contributor(s): IEEE Xplore (Online Service) [distributor.] | MIT Press [publisher.].
Material type:
BookPublisher: Cambridge, Massachusetts : MIT Press, c2002Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2002]Description: 1 PDF (xiv, 306 pages) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9780262267090.Subject(s): Computer vision | COMPUTERS -- Computer Vision & Pattern RecognitionGenre/Form: Electronic books.Online resources: Abstract with links to resource Also available in print.Summary: Two important subproblems of computer vision are the detection and recognition of 2D objects in gray-level images. This book discusses the construction and training of models, computational approaches to efficient implementation, and parallel implementations in biologically plausible neural network architectures. The approach is based on statistical modeling and estimation, with an emphasis on simplicity, transparency, and computational efficiency.The book describes a range of deformable template models, from coarse sparse models involving discrete, fast computations to more finely detailed models based on continuum formulations, involving intensive optimization. Each model is defined in terms of a subset of points on a reference grid (the template), a set of admissible instantiations of these points (deformations), and a statistical model for the data given a particular instantiation of the object present in the image. A recurring theme is a coarse to fine approach to the solution of vision problems. The book provides detailed descriptions of the algorithms used as well as the code, and the software and data sets are available on the Web.
Includes bibliographical references (p. [287]-297) and index.
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Two important subproblems of computer vision are the detection and recognition of 2D objects in gray-level images. This book discusses the construction and training of models, computational approaches to efficient implementation, and parallel implementations in biologically plausible neural network architectures. The approach is based on statistical modeling and estimation, with an emphasis on simplicity, transparency, and computational efficiency.The book describes a range of deformable template models, from coarse sparse models involving discrete, fast computations to more finely detailed models based on continuum formulations, involving intensive optimization. Each model is defined in terms of a subset of points on a reference grid (the template), a set of admissible instantiations of these points (deformations), and a statistical model for the data given a particular instantiation of the object present in the image. A recurring theme is a coarse to fine approach to the solution of vision problems. The book provides detailed descriptions of the algorithms used as well as the code, and the software and data sets are available on the Web.
Also available in print.
Mode of access: World Wide Web
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