Markov random field modeling in computer vision
Markov random field modeling in computer vision
Image Registration Guided by Particle Filter
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Phase correlation based image alignment with subpixel accuracy
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
| Hi-index | 0.00 |
Image Registration is central to different applications such as medical analysis, biomedical systems, image guidance, etc. In this paper we propose a new algorithm for multi-modal image registration. A Bayesian formulation is presented in which a likelihood term is defined using an observation model based on linear intensity transformation functions. The coefficients of these transformations are represented as prior information by means of Markov random fields. This probabilistic approach allows one to find optimal estimators by minimizing an energy function in terms of both the parameters that control the affine transformation of one of the images and the coefficient fields of the intensity transformations for each pixel.