Adaptive thresholding: A comparative study

Abstract — With the growth of image processing applications, image segmentation has become an important part of image processing. The simplest method to segment an image is thresholding. Using the thresholding method, segmentation of an image is done by fixing all pixels whose intensity values are more than the threshold to a foreground value. The remaining pixels are set to a background value. Such technique can be used to obtain binary images from grayscale images. The conventional thresholding techniques use As previously noted, recently a number of worksa global threshold for all pixels, whereas done adaptive thresholding changes the threshold value dynamically over the image. This paper offers a comparative study on adaptive thresholding techniques to choose the accurate method for binarizing an image based on the contrast, texture, resolution etc. of an image. Keywords —Threshold, Otsu’s Method, Kapur’s threshold, Rosin’s threshold, Entropy based thresholding,