Authors:
Madjid Moghrani
;
Claude Cariou
and
Kacem Chehdi
Affiliation:
TSI2M Laboratory, University of Rennes 1 / ENSSAT, France
Keyword(s):
Image segmentation, Classification, Detection, Texture features, Adaptive segmentation, Multi-component imagery.
Related
Ontology
Subjects/Areas/Topics:
Image and Video Processing, Compression and Segmentation
;
Multidimensional Signal Processing
;
Multimedia
;
Multimedia Signal Processing
;
Obstacles
;
Remote Sensing
;
Sensor Networks
;
Telecommunications
Abstract:
We present a cooperative and adaptive system for multi-component image segmentation, in which segmentation methods used are based upon the classification of pixels represented by statistical features chosen with respect to the nature of the regions to segment. One originality of this system is its adaptive characteristic: it allows taking into account the local context in the image to automatically adapt the segmentation process to the nature of specific regions which can be uniform or textured. The method used for the detection of the regions’ nature is based on a classification of pixels with respect to the uniformity index of Haralick. Then a cooperative approach is set up for the textured areas which can combine results incoming from different classification methods and choose the best result at the pixel level using an assessment index. In order to validate the system and show the relevance of the adaptive procedure used, experimental results are presented for the segmentation o
f synthetic and real multi-component CASI images.
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