By Topic

A Bayesian approach to object detection in color images

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Celenk, M. ; Sch. of Electr. & Comput. Eng., Ohio Univ., Athens, OH, USA

In this paper, we describe a supervised parametric Bayesian approach to object detection problem in color images. The proposed method makes use of multivariate Gaussian approximation to color distributions of different image regions. First, a set of sample points with known classification is collected from the object and background areas of an input image. This labeled training set is used for maximum likelihood (ML) estimates of the color mean vectors and covariance matrices for the regions of interest. A decision function is then defined as the a posteriori class conditional probabilities of the object and background regions. Using the Bayes rule and Gaussian models, a quadratic decision function is obtained corresponding to a hyperquadratic decision surface in the (R,G,B)-measurement space. The underlying surface divides the space into two mutually exclusive volumes of the object and background color clusters, resulting in a pixel classification strategy in the spatial plane. The experimental result provided in the paper illustrates that the method is quite effective on noisy textured images

Published in:

System Theory, 1998. Proceedings of the Thirtieth Southeastern Symposium on

Date of Conference:

8-10 Mar 1998