| Paper: | IMDSP-P11.5 | ||
| Session: | Image Segmentation | ||
| Time: | Thursday, May 18, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Image and Multidimensional Signal Processing: Image Segmentation | ||
| Title: | IMPROVED IMAGE SEGMENTATION WITH A MODIFIED BAYESIAN CLASSIFIER | ||
| Authors: | Thomas Weldon, University of North Carolina, Charlotte, United States | ||
| Abstract: | A method for improving texture segmentation results by slightly modifying the decision surfaces of a Bayesian classifier is presented. Although a Bayesian classifier provides optimum classification within homogeneous regions, it does not necessarily provide accurate localization of region boundaries. In the proposed method, a modified classifier is formed by using a mixture probability density. This approach has the advantage that it is easily implemented in multidimensional classifiers such as those used in classifying the vector output of a filter bank. Experimental results demonstrate improved texture segmentation using the proposed classifier. | ||
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