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Proceedings Paper

Object detection in color images using nonparametric Bayes classification and orthogonal functions
Author(s): Mehmet Celenk; Yuan Shao
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Paper Abstract

In this paper, we present a supervised, nonparametric Bayesian approach to object detection problem in color images. The proposed method does not assume any a priori knowledge about the object color distributions and their forms. It estimates the underlying color densities directly from the given image data. In this respect, the algorithm is not affected by substantial variations in the input scenes. For density estimation, the object color distributions are expanded in a series of orthogonal basis functions in the (R,G,B)-color space. The basis functions selected are the Hermite polynomials since their region of orthogonality spans the entire red, green, and blue coordinate axes. Several color images of complex shaped objects are processed by the detection method in the computer simulation. The input images are varied in size to determine the computational cost of the algorithm. During the training phase, the object and background color densities of the selected images are estimated using the first seventeen Hermite polynomials. In the classification phase, the nonparametric Bayesian classifier is applied to the input images to detect the objects of interest. Even for noisy images of textured scenes, the algorithm has detected the regions of interest with high accuracy. The experimental results indicate that the seventeen lowest-order Hermite polynomials are sufficient for accurate color density estimation. The computational cost of the method is also reasonably low for this type of iterative density estimation process.

Paper Details

Date Published: 6 July 1998
PDF: 9 pages
Proc. SPIE 3387, Visual Information Processing VII, (6 July 1998); doi: 10.1117/12.316403
Show Author Affiliations
Mehmet Celenk, Ohio Univ. (United States)
Yuan Shao, Ohio Univ. (United States)

Published in SPIE Proceedings Vol. 3387:
Visual Information Processing VII
Stephen K. Park; Richard D. Juday, Editor(s)

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