Noise insensitive contrast enhanced adaptive global maximum clustering based multiple region segmentation

Multiple region image segmentation aims to partition a given image into several meaningful regions based on certain attributes such as intensity, texture, color, etc. A multiple-region segmentation problem is unstable because detection of number of regions is done manually. This is one of the most challenging and important problems in computer vision. This project present a region based method for joint clustering of multiple image segmentations. This proposes new method of segmenting an image into several sets of pixels with similar intensity values. To solve this problem use an Adaptive Global Maximum Clustering method this automatically finds the number of regions using image histogram. The proposed method is able to find the reasonable number of distinct regions not only for clean images but also for noisy ones. This method is made up of two procedures. First, develop the adaptive global maximum clustering. In this method use image histogram and automatically obtain the number of significant local maxima of the histogram which indicates the number of different regions in the image. Second, derive a simple and fast calculation to segment an image into distinct multiple regions.