Comparative Study on Segmentation Using Texture Models

Image segmentation is the process of partitioning digital images into multiple segments sets of pixels for the purpose of being able to analyze that image for certain features. The field of "computer vision" and image segmentation was born around the mid 1960s - almost a decade before the introduction of personal computers! Automatic segmentation techniques have come a long way since the 1960s, however there is a very long way still to go.There are many different ways to perform image segmentation, including Thresholding methods, such as Otsu's method; Clustering methods, such as K-means and principle components analysis; Transform methods, such as watershed; Texture methods, such as texture filters. This book elaborately discuss about texture method, the inherent assumptions of different approaches make about, what constitutes a good segment, and also emphasize general mathematical tools that are promising.