A Survey Paper on Image Segmentation with Thresholding?

In computer vision, image segmentation is the process of partitioning a digital image into multiple sections. The goal of segmentation is to simplify and/or change the representation of an image into something that is more important and easier to examine. Image segmentation is typically used to locate objects and background in images. More exactly, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. Image segmentation is an important signal processing tool that is widely employed in many applications including object detection, object-based coding, object tracking, image retrieval, and clinical organ or tissue identification. Thresholding is the basic method of image segmentation. From a grayscale image, thresholding can be used to generate binary images. The idea of this method is to select the threshold value. A number of accepted methods are used in engineering including the maximum entropy method, Otsu's method that uses maximum variance, and k-means clustering. The main idea is that, the proposed segmentation can be work effectively for image based on automatic thresholding and color model based image segmentation.