Image segmentation based on cooccurrence matrix edge information

Abstract

Thresholding techniques are segmentation technique used to segment images consisting of dark objects against bright backgrounds, or vice versa. It also offers data compression and fast data processing [1]. The simplest way is through a technique called global thresholding, where one threshold value is selected for the entire image, which is obtained from the global information. However, when the background has non-uniform illumination, a fixed (or global) threshold value will poorly segment the image. Thus, a local threshold value that changes dynamically over the image is needed. This technique is called adaptive thresholding. Many works have been done to formulate the best technique for the adaptive thresholding to accommodate image conditions such as non-uniform illumination, noisy image and complex background [1-15]. Basically these techniques can be divided into region-based and edge-based thresholding. Regionbased technique uses the whole image to extract the information for the threshold value computation, while edge-based technique is based on the attriibutes along the contour between the object and the background.