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Articles by U.S.N. Raju
Total Records ( 3 ) for U.S.N. Raju
  U.S.N. Raju , B. Eswara Reddy , V. Vijaya Kumar and B. Sujatha
  A novel method for dominant skeleton extraction of textures using different wavelet transforms, is proposed in this study. The skeleton varies depending on the shape of structuring element. If the structuring element is homothetic to the object, the object is covered with only one magnification of the structuring element. By this, the skeleton is reduced to one point. The present study considers the skeleton from a binary texture. The proposed method derives from the above that a total number of pixels within the skeleton is the minimum when structuring element is homothetic to the primitive. This provides the scope that the texture is composed of one primitive, which minimizes the total number of pixels. For evaluating such skeleton primitive the present study utilized a 3x3 structuring element, as the skeleton primitives. All possible skeleton primitives combinations of 3x3 mask are evaluated on all textures. The skeleton primitive that is making the least number of skeleton points is considered as dominant skeleton primitive. Based on the extraction of skeleton primitives a classification is made on textures using Haar, Daubechies, Coiflet and Symlet wavelets. Experimental results indicate a good classification and also a comparison is made among these four wavelet results. Present method is experimented on Brodatz textures using these four wavelets.
  V. Vijaya Kumar , U.S.N. Raju , P. Premchand and A. Suresh
  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.
  V.Vijaya Kumar , B.Eswara Reddy , U.S.N. Raju and K.Chandra S ekharan
  The present paper proposes a method of texture classification based on long linear patterns. Linear patterns of long size are bright features defined by morphological properties: linearity, connectivity, width and by a specific Gaussian-like profile whose curvature varies smoothly along the crest line. The most significant information of a texture often appears in the occurrence of grain components. That’s why the present paper used sum of occurrence of grain components for feature extraction. The features are constructed from the different combination of long linear patterns with different orientations. These features offer a better discriminating strategy for texture classification. Further, the distance function captured from the sum of occurrence of grain components of textures is expected to enhance the class seperability power. The class seperability power of these features is investigated in the classification experiments with arbitrarily chosen texture images taken from the Brodatz album. The experimental results indicated good analysis, and how the classification of textures will be effected with different long linear patterns.
 
 
 
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