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Journal of Computer Science
Year: 2008  |  Volume: 4  |  Issue: 7  |  Page No.: 591 - 599

Skeleton Primitive Extraction Method on Textures with Different Nonlinear Wavelets

V. Vijaya Kumar, U.S.N. Raju, P. Premchand and A. Suresh    

Abstract: Problem Statement: A novel method for dominant skeleton extraction of textures using different nonlinear wavelet transforms is proposed in this study. In the present study 3×3 masks are used for extraction of skeleton primitives. For a 3×3 skeleton primitive there will be 29 skeleton primitive combinations. But the present study considered a skeleton primitive for the skeletonization purpose if and only if its center pixel is one and skeleton primitives are represented by the corresponding skeleton primitive weight. By this, there will be 28 combinations of skeleton primitives. Approach: The skeleton primitive were used for evaluating skeleton points. The skeleton of an object has the property that it was reduced to one point when the skeleton primitive used for the skeletonization is exactly homothetic to the object. The dominant skeleton subset was evaluated by counting the skeleton points. The skeleton subset that leads to the least skeleton points will be the resultant skeleton subset. The present study classified the textures based on two methods. In the first method textures are classified based on skeleton primitive weight, which was nothing but based on skeleton primitive combination. In the second method classification is made based on distance function of skeleton points. Results: The proposed method was applied on 24 Brodatz textures using the three nonlinear wavelet transformed textures. By this the dominant skeleton primitive weight is obtained for each texture. Based on the number of skeleton points distance measures were calculated based on which texture classification is obtained. Conclusions: The two methods were applied on Brodatz textures using different nonlinear wavelet transforms which classified the textures. The first method was appropriate if one need to classify based on skeleton subsets. The second method was appropriate if the classification is to be done based on least number of skeleton points.

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