Morphological Image Segmentation Methods for Textures
Basics, Concepts, Design algorithms and Implementations, comparisons of algorithms
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Over the last few years, the use of automated machines has increased exponentially in the area of medical diagnosis, smoke detection and agricultural applications. Texture segmentation plays a major role in all these applications. Enormous and in depth research has taken place on image segmentation still there is a need to extract segmented regions precisely. That's why lot of research is going on image segmentation. Mathematical Morphology's geometry-oriented nature provides a well organized and capable structure for studying object shape properties such as connectivity, topology and size whi...
Over the last few years, the use of automated machines has increased exponentially in the area of medical diagnosis, smoke detection and agricultural applications. Texture segmentation plays a major role in all these applications. Enormous and in depth research has taken place on image segmentation still there is a need to extract segmented regions precisely. That's why lot of research is going on image segmentation. Mathematical Morphology's geometry-oriented nature provides a well organized and capable structure for studying object shape properties such as connectivity, topology and size which cannot be simply retrieved by other approaches. The advantages of morphological treatment is region filling, boundary detection, noise filtering, thickening, skeletonizing, quantitative description of objects (projections, area, perimeter), object marking, shape simplification, thinning, convex hull, object segmentation from background. Based on this, morphological treatment is applied onthe segmentation methods
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