Abstract
The optimal features with which to discriminate between regions and, thus, segment an image often differ depending on the nature of the image. Many real images are made up of both smooth and textured regions and are best segmented using different features in different areas. A scheme that automatically selects the optimal features for each pixel using wavelet analysis is proposed, leading to a robust segmentation algorithm. An automatic method for determining the optimal number of regions for segmentation is also developed.
| Translated title of the contribution | A robust automatic clustering scheme for image segmentation using wavelets |
|---|---|
| Original language | English |
| Pages (from-to) | 662 - 665 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 5 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Apr 1996 |
Bibliographical note
Terms of use: Copyright © 1996 IEEE. Reprinted from IEEE Transactions on Image Processing.This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected].
By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)