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Wavelet based Texture Segmentation methods based on Combinatorial of Morphological and Statistical Operations


V. Vijaya Kumar, U.S.N.Raju, M. Radhika Mani, A.L.Narasimha Rao


Vol. 8  No. 8  pp. 176-181


Texture analysis such as segmentation and classification plays a vital role in computer vision and pattern recognition and is widely applied to many areas such as industrial automation, bio-medical image processing and remote sensing. A segmentation scheme based on combinations of morphological and statistical operations is introduced in this paper on wavelet transformed images. Mathematical morphology is very attractive for this purpose because it efficiently deals with geometrical features like size, shape, contrast or connectivity that can be considered as segmentation oriented features. Derived equations on dilation, erosion and median or mean which finally results segmentation were applied on Harr, Db6, Cf6 and Sym8 wavelet transformed images. The present paper divides the wavelet combinatorial segmentation algorithm into three groups based on number of operations and type of operations, used. The present method using wavelet transforms is applied on Brodatz textures and a good segmentation is resulted.


Geometrical features, Dilation, Erosion, Mean, Median, Dynamic, Number of operations and Type of operations