Based On The Consistency Measure Image Segmentation Algorithm

Image processing and pattern recognition is a cutting-edge interdiscipline. It is well known that edge detection, image segmentation and object description are the mainly research subjects in this field. In addition, image segmentation plays a very important role in the field of image processing and pattern recognition, and it is classical difficult problem of the lower level computer vision as well as the fundamental step for image understanding.We focus on the study of image segmentation based on homogeneity measure and our primary work is: (1) we propose a method for image edge detection based on homogeneity measure. In this method, we detect edges by the value of homogeneity measure function. This method is combined with the CattPM equation for noise eliminating and edge-thinning technique for correctness of edge locating. (2) we propose a region-merging method for image segmentation based on homogeneity measure. We design two kinds of region-merging arithmetic. Additionally, we compare our method with a traditional method using histogram criterion. (3) we propose a method of texture image segmentation based on feature weighting using homogeneity measure. This method improves correctness of segmentation by weighting futures extracted from texture image.