Segmentation of multi-spectral images using the combined classifier approach

✍ Scribed by P. Paclı́k; R.P.W. Duin; G.M.P. van Kempen; R. Kohlus

• Publisher: Elsevier Science
• Year: 2003
• Tongue: English
• Weight: 687 KB
• Volume: 21
• Category: Article
• ISSN: 0262-8856
• DOI: 10.1016/s0262-8856(03)00013-1

✦ Synopsis

Segmentation methods, combining spectral and spatial information, are essential for analysis of multi-spectral images. In this article, we propose such a method based on statistical pattern recognition algorithms and a combined classifier approach. A set of experiments is presented with multi-spectral images of detergent laundry powders acquired by imaging cross-sections with scanning electron microscopy using energy-dispersive X-ray microanalysis (SEM/EDX). The algorithm stability and the segmentation quality are investigated. The use of a priori information for the segmentation of images with similar spectral properties is studied as well. Finally, a comparison with probabilistic relaxation method for multi-spectral image segmentation is made.