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A probabilistic matching algorithm for computer vision

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Abstract

A model-based vision system attempts to find correspondences between features of an object model and features detected in an image for purposes of recognition, localization, or inspection. In this paper we pose the relational matching problem as a special case of the pattern complex recognition problem and propose a probabilistic model to describe the images of an object. This Bayesian approach allows us to make explicit statements of how an image is formed from a model, and hence define a natural matching cost that can be used to guide a heuristic search in finding the best observation mapping. Furthermore, we show that even though the nature of the feature matching problem is exponential, the use of the proposed algorithm keeps the size of the problem under control, by efficiently reducing the search space.

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Authors and Affiliations

  1. Department of Electrical Engineering and Department of Computer Science and Engineering, The Pennsylvania State University, 16802, University Park, PA, USA

    Octavia I. Camps

  2. Department of Computer Science, FR-35, University of Washington, 98195, Seattle, WA, USA

    Linda G. Shapiro

  3. Electrical Engineering Department, FT-10, University of Washington, 98195, Seattle, WA, USA

    Robert M. Haralick

Authors
  1. Octavia I. Camps
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  2. Linda G. Shapiro
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  3. Robert M. Haralick
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Camps, O.I., Shapiro, L.G. & Haralick, R.M. A probabilistic matching algorithm for computer vision. Ann Math Artif Intell 10, 85–124 (1994). https://doi.org/10.1007/BF01530945

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