Two dimensional histogram analysis using the Helmholtz principle

An algorithm for two dimensional histogram modal analysis is presented. A major challenge in two dimensional histogram analysis is to provide an accurate location and description of the extended modal shape. The approach presented in this paper combines the Fast Level Set Transform of the histogram and the Helmholtz principle to find the location and shape of the modes. Furthermore, the algorithm is devoid of any a priori assumptions about the underlying density or the number of modes. At the core, this approach is a new way to manage and search the number of regions that must be examined to identify meaningful sets. Computational issues required a new tail sum bound on the multinomial distribution to be stated and proven. This bound reduces to the Höeffding inequality for the binomial distribution. The histogram segmentation procedure was applied to the two problems of image color segmentation and correlation pattern recognition. With no a priori knowledge about the color image assumed, the two dimensional modal analysis is applied to the CIELAB color space to find perceptually uniform dominant colors. The modal analysis is also extended to correlation pattern recognition to find multiple targets in a single correlation plane.