Adaptive local thresholding algorithm that maximizes the contour features within the thresholded image

Abstract

Global thresholding is widely used in image processing to generate binary images, which are used by various pattern recognition systems. Typically, many features that are present in the original gray-level image are lost in the resulting binary image. This paper presents an adaptive thresholding algorithm, that maximizes the edge features within the gray-level image. The Gaussian pyramid algorithm is used to find the local gray-level variations that are present in the original gray-level image. The resulting Gaussian pyramid image is then subtracted from the original gray-level image removing the local variations in illumination. This new image is then adaptively thresholded using the adaptive contour entropy algorithm. The resulting binary images have been shown to contain more edge features than the binary images generated using global thresholding techniques.