Share Email Print
cover

Optical Engineering

Color textile image segmentation based on multiscale probabilistic reasoning
Author(s): Xiqun Lu
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Color textile images usually show a few dominant colors, and the inherent fabric thread structure makes it a difficult job for automatic clustering-based techniques to extract dominant colors from textile images. Based on the two distinctive features of textile images, a probabilistic reasoning segmentation algorithm for color textile images is proposed. Due to the uniform texture of the fabric appearing in textile images, dominant colors are extracted interactively. Then a hierarchic probabilistic reasoning model is applied to capture not only the statistical dependences of color information across adjacent scales, but also those among intrascale neighbor blocks. The multiscale approach is used to avoid the conflict between boundary localization and high-resolution segmentation by deducing the maximum posterior probability for each block recursively from coarse to fine scale. No special prior distribution assumption is made about the size and shape of regions in this algorithm. That there is no need to train the multiscale contextual model prior to the segmentation is one of the big advantages of this approach. Experimental results show that the proposed algorithm can produce better segmentation results and smoother edge maps for color textile images than some state-of-the-art segmentation techniques, both supervised and nonsupervised.

Paper Details

Date Published: 1 August 2007
PDF: 9 pages
Opt. Eng. 46(8) 087001 doi: 10.1117/1.2768319
Published in: Optical Engineering Volume 46, Issue 8
Show Author Affiliations
Xiqun Lu, Zhejiang Univ. (China)


© SPIE. Terms of Use
Back to Top