Integration of intensity-based and edge-based stereo algorithms using Markov random fields

Summary form only given, as follows. A method is introduced where image intensity and edge information from a pair of stereo images are integrated into a single stereo vision technique. A Bayesian model is used to derive the maximum a posteriori (MAP) stereo matched solution for the proposed integrated matching algorithm. The disparity is modeled as a Markov random field (MRF) and the input image data as a coupled MRF (intensity and edge orientation process together). The left and right stereo images are considered as degraded observations and external inputs to the system. The well-known MRF-Gibbs distribution equivalence is used to reduce the MAP problem to that of finding an appropriate energy function (cost function) that describes the constraints on the solution. A stochastic relaxation algorithm (simulated annealing) is used to find the best disparity solution by minimizing the energy equation. Results are presented for the proposed integrated stereo technique.<>