Markovian Energy-Based Computer Vision Algorithms on Graphics Hardware

This paper shows how Markovian segmentation algorithms used to solve well known computer vision problems such as motion estimation, motion detection and stereovision can be significantly accelerated when implemented on programmable graphics hardware. More precisely, this contribution exposes how the parallel abilities of a standard Graphics Processing Unit (usually devoted to image synthesis) can be used to infer the labels of a label field. The computer vision problems addressed in this paper are solved in the maximum a posteriori (MAP) sense with an optimization algorithm such as ICM or simulated annealing. To do so, the fragment processor is used to update in parallel every labels of the segmentation map while rendering passes and graphics textures are used to simulate optimization iterations. Results show that impressive acceleration factors can be reached, especially when the size of the scene, the number of labels or the number of iterations is large. Hardware results have been obtained with programs running on a mid-end affordable graphics card.