Modeling Stereopsis via Markov Random Field

Abstract

Markov random field (MRF) and belief propagation have given birth to stereo vision algorithms with top performance. This article explores their biological plausibility. First, an MRF model guided by physiological and psychophysical facts was designed. Typically an MRF-based stereo vision algorithm employs a likelihood function that reflects the local similarity of two regions and a potential function that models the continuity constraint. In our model, the likelihood function is constructed on the basis of the disparity energy model because complex cells are considered as front-end disparity encoders in the visual pathway. Our likelihood function is also relevant to several psychological findings. The potential function in our model is constrained by the psychological finding that the strength of the cooperative interaction minimizing relative disparity decreases as the separation between stimuli increases. Our model is tested on three kinds of stereo images. In simulations on images with repetitive patterns, we demonstrate that our model could account for the human depth percepts that were previously explained by the second-order mechanism. In simulations on random dot stereograms and natural scene images, we demonstrate that false matches introduced by the disparity energy model can be reliably removed using our model. A comparison with the coarse-to-fine model shows that our model is able to compute the absolute disparity of small objects with larger relative disparity. We also relate our model to several physiological findings. The hypothesized neurons of the model are selective for absolute disparity and have facilitative extra receptive field. There are plenty of such neurons in the visual cortex. In conclusion, we think that stereopsis can be implemented by neural networks resembling MRF.