Noisy Binary Textura Recognition Using the Coordinated Cluster Transform

IN THIS PAPER TECHNIQUE USING THE COORDINATED CLUSER REPRESENTATION (CCR) IS EXAMINED FOR RECOGNITION OF BINARY COMPUTER GENERATED AND NATURAL TEXTURE IMAGES CORRUPTED BY ADDITIVE NOISE. A NORMALIZED LOCAL PROPERTY HISTOGRAM OF THE CCR IS USED AS A UNIQUE FEATURE VECTOR. THE ABILITY OF THE DESCRPTIOR TO CAPTURE SPATIAL STATISTICAL FEATURES OF AN IMAGE IS EXPLOTED. THE EVALUATION CRITERIA IS THE RECOGNITION PERFORMANCE USING A SIMPLE MINIMUN DISTANCE CLASSIFIER FOR RECOGNITION PURPOSE. THE EXPERIMENTAL RESULTS INDICATE THAT THE PROPOSED TECHNNIQUE IS EFFICIENT FOR RECOGNITION OF TEXTURES DETERIORATED BY HIGT LEVEL ADDITIVE NOISE. TEXTURES UNDER TEST RUN THROUGH PERIODIC UP TO RANDOM ONES

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