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Proceedings Paper

Object detection and recognition using evidences-based image analysis
Author(s): Yury V. Visilter; Sergey Yu. Zheltov; Alexander A. Stepanov
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Paper Abstract

The generic technique called the 'Evidences-based Image Analysis' is proposed for a model-based object detection. Real images to be analyzed are considered as the sources of evidences generated by the procedures of low-level image processing. These evidences support or refute hypothesis connected with different objects and their features. The Bayesian theorem is of use for hypothesis testing by evidences. The unknown parameters of probabilistic model are used as the internal parameters of algorithm tuning. This approach provides the most uniform and efficient way for the fusion of any available image information: intensity and contour, 2D and 3D, multispectral, multisensor and so on. Our technique takes into account three principal points: object/background model, registration model and corruption model. This paper concentrates mainly on the registration parameters' estimation, especially on the problem of geometrically invariant object detection. It is shown that the Hough-like accumulation methods really implement the maximum a posteriori estimation of the parameters of registration model under the assumption of statistical independence of evidences. The reduction and separation of models are proved to be the legal ways for fastening of the invariant object detection. The usage of complex hierarchical models of objects is considered as another way for fast invariant detection and recognition.

Paper Details

Date Published: 8 October 1996
PDF: 10 pages
Proc. SPIE 2823, Statistical and Stochastic Methods for Image Processing, (8 October 1996); doi: 10.1117/12.253444
Show Author Affiliations
Yury V. Visilter, State Research Institute of Aviation Systems (Russia)
Sergey Yu. Zheltov, State Research Institute of Aviation Systems (Russia)
Alexander A. Stepanov, State Research Institute of Aviation Systems (Russia)

Published in SPIE Proceedings Vol. 2823:
Statistical and Stochastic Methods for Image Processing
Edward R. Dougherty; Francoise J. Preteux; Jennifer L. Davidson, Editor(s)

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