Probabilistic estimation of local scale

We present a probabilistic approach for local scale selection. The proposed method computes a probability measure on scale space, which is based on Bayesian estimation theory, and it leads to an efficient computational implementation. At each scale we associate a decomposition likelihood, one for the smoothed image and other for the residual. The scale selection method, based on the minimal description length principle, maximizes the likelihood of the observed image given the local scale, and at the same time, minimizes the residual. Initial experiments show that our approach can be successfully applied to edge detection, and also to adaptive Gaussian filtering and texture segmentation.