SEGMENTATION USING FUZZY LOGIC IN COLOR IMAGES BASED ON MEMBERSHIP FUNCTIONS

Color Image Segmentation is the high level image description in terms of objects, scenes, and features separates the image into distinct regions of similar pixels based on pixel property. The success of image analysis depends on segmentation reliability. This article presents a novel approach for color image segmentation using two different algorithms with respect to color features. Here presented an adaptive masking method based on fuzzy membership functions and a thresholding mechanism over each color channel to overcome over segmentation problem, before combining the segmentation from each channel into the final one. Our proposed method ensures accuracy and quality of different kinds of color images. Consequently, the proposed modified fuzzy approach can enhance the image segmentation performance by use of its membership functions. Similarly, it is worth noticing that our proposed approach is faster than many other segmentation algorithms, which makes it appropriate for real-time application. According to the visual and quantitative authentication, the proposed algorithm is performing better than existing algorithms.