We attempt to unify several approaches to image segmentation in early vision under a common framework. The Bayesian approach is very attractive since: (i) it enables the assumptions used to be explicitly stated in the probability distributions, and (ii) it can be extended to deal with most other problems in early vision. Here, we consider the Markov random field formalism, a special case of the Bayesian approach, in which the probability distributions are specified by an energy function. We show that: (i) our discrete formulations for the energy function is closely related to the continuous formulation; (ii) by using the mean field (MF) theory approach, introduced by Geiger and Girosi , several previous attempts to solve these energy functions are effectively equivalent; (iii) by varying the parameters of the energy functions we can obtain connections to nonlinear diffusion and minimal description length approaches to image segmentation; and (iv) simple modifications to the energy can give a direct relation to robust statistics or can encourage hysteresis and nonmaximum suppression.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Artificial Intelligence