Disparity Estimation by Pooling Evidence From Energy Neurons

In this paper, we propose an algorithm for disparity estimation from disparity energy neurons that seeks to maintain simplicity and biological plausibility, while also being based upon a formulation that enables us to interpret the model outputs probabilistically. We use the Bayes factor from statistical hypothesis testing to show that, in contradiction to the implicit assumption of many previously proposed biologically plausible models, a larger response from a disparity energy neuron does not imply more evidence for the hypothesis that the input disparity is close to the preferred disparity of the neuron. However, we find that the normalized response can be interpreted as evidence, and that information from different orientation channels can be combined by pooling the normalized responses. Based on this insight, we propose an algorithm for disparity estimation constructed out of biologically plausible operations. Our experimental results on real stereograms show that the algorithm outperforms a previously proposed coarse-to-fine model. In addition, because its outputs can be interpreted probabilistically, the model also enables us to identify occluded pixels or pixels with incorrect disparity estimates.