A Survey on Threshold Based Segmentation Technique in Image Processing

The present paper describes the study of the threshold techniques in image segmentation. Image segmentation is one of the fundamental approaches of the digital image processing. Image segmentation is used widely in many applications. Several general purpose algorithms and techniques have been developed for image segmentation. Segmentation applications are involving detection, recognition and measurement of features. The purpose of image segmentation is to partition an image into meaningful regions with respect to a particular application. Segmentation techniques can be classified as either contextual or non-contextual. Thresholding is a Non-Contextual Approach. This method is based on a threshold value to turn a gray-scale image into a binary image. In Histogram Dependent Technique, a histogram is computed from all of the pixels in the image and this paper enumerates and reviews a comparative performance of threshold technique as Histogram Dependent Technique (HDT) based on Global Threshold, Local Threshold and Adaptive Threshold one another.