Get 2D Object Detection and Recognition: Models, Algorithms, and PDF

By Yali Amit

ISBN-10: 0262011948

ISBN-13: 9780262011945

Very important subproblems of computing device imaginative and prescient are the detection and popularity of 2nd gadgets in gray-level photographs. This e-book discusses the development and coaching of types, computational methods to effective implementation, and parallel implementations in biologically believable neural community architectures. The technique relies on statistical modeling and estimation, with an emphasis on simplicity, transparency, and computational efficiency.The ebook describes a number deformable template versions, from coarse sparse versions regarding discrete, quickly computations to extra finely certain versions in keeping with continuum formulations, related to extensive optimization. each one version is outlined when it comes to a subset of issues on a reference grid (the template), a collection of admissible instantiations of those issues (deformations), and a statistical version for the knowledge given a selected instantiation of the thing found in the picture. A habitual subject is a rough to nice method of the answer of imaginative and prescient difficulties. The e-book offers exact descriptions of the algorithms used in addition to the code, and the software program and information units can be found at the Web.

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Extra resources for 2D Object Detection and Recognition: Models, Algorithms, and Networks

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21 for s − 1.

11 is obtained in the same manner. A change in the orientation of the curves would change the signs in both equations. 2 An Edge-Based Data Model The original work on deformable contours in the computer-vision literature, Kass, Witkin, and Terzopoulos (1987), did not employ a statistical model. Rather, a cost function was directly formulated in which the data term evaluates how consistent the curve is with the edges in its neighborhood. The original approach simply integrated the magnitude of the image gradient along the curve.

For significantly larger scales, the image is down sampled and the same procedure is implemented. 1. The intuition is that a linear transformation of the model is smoothly deformed to produce the instantiation of the object. The set ϒ is defined through some finite dimensional parameterization of nonlinear deformations of the set Z , and a prior is defined that penalizes large deviations from the identity map. The initial location and linear map from A ∈ A are provided by the user. This defines an initial instantiation θ0,i = x0 + Az i , i = 1, .

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2D Object Detection and Recognition: Models, Algorithms, and Networks by Yali Amit


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