University of Malta
 

Unsupervised Texture Segmentation
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Image segmentation represents one of the earliest stages of high-level vision and leads to image understanding.  Most images would contain regions characterized by texture and thus, texture analysis is often considered necessary for effective image segmentation. We have focused on the problem of unsupervised texture segmentation with an unknown number of texture regions based on features extracted from a multiresolution Gabor expansion scheme. Since the scheme generates a large number of features, the two-point correlation function was used to test the separability of feature distributions and select appropriate features. The segmentation progresses from a coarse resolution level to the finest resolution level. Two algorithms were developed to refine the boundary between different textures, one method using a mixed pixel classification scheme and the other using a multiresolution probabilistic relaxation scheme resulting in a better localized region boundary.
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Funding Award for BrainApp
CBC awarded research funding under the FUSION Technology Development Programme (TDP) 2017

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Last Updated: 13 October 2008

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