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TRINH , MCALLESTER : UNSUPERVISED LEARNING FOR STEREO 1 Unsupervised Learning of Stereo Vision with Monocular Cues

We demonstrate unsupervised learning of a 62 parameter slanted plane stereo vision model involving shape from texture cues. Our approach to unsupervised learning is based on maximizing conditional likelihood. The shift from joint likelihood to conditional likelihood in unsupervised learning is analogous to the shift from Markov random fields (MRFs) to conditional random fields (CRFs). The performance achieved with unsupervised learning is close to that achieved with supervised learning for this model.