Optimal thresholding for color images

Abstract

Color image thresholding is a special case of color clustering which is commonly used for tasks such as object detection, region segmentation, enhancement, and target tracking. As compared to the three-dimensional (3-D) color clustering, thresholding is computationally more efficient for computer implementation and pipelined hardware realization. Traditionally, this method operates on a particular color component whose distribution possesses more prominent peaks than the other two color histograms. In this operation, it is expected that the histogram peaks represent meaningful object areas. However, the color component thresholding results are less reliable than those of 3-D clustering because the valuable information in the other two color components are ignored in region acceptance process. To improve the performance of thresholding, we describe a method that thresholds an input image three times on three different color components independently. The best thresholds are selected by optimizing the within-group variance or directed divergence measure for red, green, and blue distributions separately. The resultant three binary images are combined by means of a predicate logic function that makes use of a 3-input, 1-output majority logic gate. This enables 1-D thresholding mechanism to incorporate the information on all the color components in region acceptance process.