Multiresolution adaptive image segmentation based on global and local statistics
In a previous work we have presented an adaptive segmentation algorithm of dirty images in a Bayesian framework. The segmentation problem is formulated as a maximum a posteriori (MAP) estimation problem. The optimization is achieved using Besag's iterated conditional modes algorithm. A multiresolution implementation of the segmentation algorithm, using the discrete wavelet transform, has been used. This work focuses on the adaptive character of the algorithm and discusses how global and local statistics can be taken into account in the segmentation process. We propose an improvement on the adaptivity by introducing an enhancement to control the adaptive properties of the segmentation process. A weighting function taking into account both local and global statistics is introduced in the minimization. The new formulation of the segmentation problem allows us to control the effective contribution of each statistic. Results of segmentation carried out on synthetic images are presented.