In this work, we propose to use an Inhomogeneous Gaussian Markov Random Field (IGMRF) and sparsity based priors in a regularization framework in order to estimate the dense disparity map. The IGMRF prior captures the smoothness as well as preserves sharp discontinuities and the sparsity prior captures the sparseness in the disparity map. We present a sparse autoencoder based approach for learning and inferring the sparse representation of disparities. An iterative two phase algorithm is proposed to solve our energy minimization problem. Experimental results on the standard datasets demonstrate the effectiveness of the proposed approach.
['Sonam Nahar', 'Manjunath V. Joshi']
A regularization framework for stereo matching using IGMRF prior and sparseness learned from autoencoder