Segmentation of multi-spectral images using the combined classifier approach

Abstract 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.

[1]  Josef Kittler,et al.  On the Foundations of Probabilistic Relaxation with Product Support , 1998, Journal of Mathematical Imaging and Vision.

[2]  David G. Stork,et al.  Pattern Classification , 1973 .

[3]  Mohamed A. Ismail,et al.  Multidimensional data clustering utilizing hybrid search strategies , 1989, Pattern Recognit..

[4]  Robert P. W. Duin,et al.  Classifier Conditional Posterior Probabilities , 1998, SSPR/SPR.

[5]  Andreas Koschan,et al.  Colour Image Segmentation: A Survey , 1994 .

[6]  Robert L. Cannon,et al.  Iterative fuzzy image segmentation , 1985, Pattern Recognit..

[7]  Markku Hauta-Kasari,et al.  Multi-spectral Texture Segmentation Based on the Spectral Cooccurrence Matrix , 1999, Pattern Analysis & Applications.

[8]  G. Lawes,et al.  Scanning Electron Microscopy and X-Ray Microanalysis , 1987 .

[9]  King-Sun Fu,et al.  A survey on image segmentation , 1981, Pattern Recognit..

[10]  J. M. Hans du Buf,et al.  A review of recent texture segmentation and feature extraction techniques , 1993 .

[11]  Chein-I Chang,et al.  Unsupervised hyperspectral image analysis with projection pursuit , 2000, IEEE Trans. Geosci. Remote. Sens..

[12]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[13]  Majid Mirmehdi,et al.  Segmentation of Color Textures , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Michael Spann,et al.  A quad-tree approach to image segmentation which combines statistical and spatial information , 1985, Pattern Recognit..

[15]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[16]  Azriel Rosenfeld,et al.  A Relaxation Method for Multispectral Pixel Classification , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Xie Yuan-dan,et al.  Survey on Image Segmentation , 2002 .

[19]  William J. Christmas,et al.  Structural Matching in Computer Vision Using Probabilistic Relaxation , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Jiri Matas,et al.  Spatial and Feature Space Clustering: Applications in Image Analysis , 1995, CAIP.

[21]  Alexander A. Sawchuk,et al.  Supervised Textured Image Segmentation Using Feature Smoothing and Probabilistic Relaxation Techniques , 1989, IEEE Trans. Pattern Anal. Mach. Intell..