COLOR IMAGE SEGMENTATION- A CROSS POINT APPROACH

Abstract

COLOR IMAGE SEGMENTATION: A CROSS POINT APPROACH. Segmentation is one of the most important steps in image processing. In the hierarchy of image engineering it lies at the center which binds the low level image processing techniques based on pixel to the higher level of image understanding layer and object recognition. Many techniques are developed that segment images based on various mathematical features of images. My main focus in this work was to develop an efficient algorithm to segment color images. In this dissertation a different technique is presented where human perception is the main motivation. Here a combination of histograms pairs is used to extract segment points. Initially color image, stored in any format, converted in RGB. Then this RGB image is converted in inverse RGB format which is used to extract histogram curves. After smoothing these histogram curves, cross points a taken out from different combinations of the cures that are ranked accordingly. These cross-points are optimized by applying filters and some variables to control the quality of the segments. During the process of segmentation not only the value of pixels are considered but the neighbors of the pixels and their association are also used which give uniqueness to this algorithm. Image set provided by the Berkeley University of California is used to train and test the proposed algorithm. For the validation of the results human judgments are also used along with the statistical tools like geometric mean, variance, and linear correlation. 100 test images are used for this purpose.