Relative entropy-based methods for image thresholding

A relative entropic thresholding approach was recently developed by Chang et al. (see Pattern Recognition, vol. 27, no. 9, p. 1275-1289, 1994). This paper extends Chang et al.´s approach to two more relative entropy-based thresholding methods, called local relative entropy thresholding (LRE) and joint relative entropy thresholding (JRE). Since relative entropy based methods are sensitive to sparse image histograms, a histogram compression and translation is suggested to compact the histogram. In order to achieve an objective assessment, uniformity and shape measures are introduced for performance evaluation. Experimental results show that when image histograms are sparse, with the proposed histogram compression and translation, JRE and LRE generally perform better than Chang et al.´s approach