Tammineni Sreelatha,M.V. Subramanyam,M. N. Giri Prasad,




Texture Classification,Steerable Filter,Gaussian Filter,Feature Computation,Feature Encoding,


Texture in images can be utilized as a cue for different computer vision tasks as object identification and classification. This paper proposes CSTC-Mel Identification Model for texture classification, the feature representation which is low dimensional and training free, robust in nature for the texture description. The proposed technique is implemented in 3 phases such as ULL responses, feature computation, Feature encoding and the representation of image. Feature Computation is generated to categorize the texture structures and their connection by implementing linear and non-linear operators on the ULL responses of Gaussian Filter in the scale space, which is established based on steerable filters. Feature encoding through more than one level of thresholding or binary can be adopted to compute these feature computation into texture. Two encoding methods are designed which is robust in nature to the illumination changes and image rotation. The feature representation is explored to combine the discrete texture into the histogram representation. Our proposed model is tested on PH2 dataset. By comparing the experimental outcomes of proposed CSTC-Mel Identification Model with existing models, we can observe t at the proposed CSTC-Mel Identification Model identifies the skin cancer with accuracy of 93.81%.


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