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The authors present a novel approach to the task of autonomous terrain classification based on structured prediction. We consider the problem of learning a classifier that will accurately segment an image into "obstacle" and "ground" patches based on supervised input. Previous approaches to this problem have focused mostly on local appearance; typically, a classifier is trained and evaluated on a pixel-by-pixel basis, making an implicit assumption of independence in local pixel neighborhoods. We relax this assumption by modeling correlations between pixels in the submodular MRF framework. We show how both the learning and inference tasks can be simply and efficiently implemented-exact inference via an efficient max flow computation; and learning, via an averaged-subgradient method. Unlike most comparable MRF-based approaches, our method is suitable for implementation on a robot in real-time. Experimental results are shown that demonstrate a marked increase in classification accuracy over standard methods in addition to real-time performance.
Markov processes, gradient methods, image classification, image segmentation, mobile robots, random processes, robot vision, terrain mapping, Markov random fields, autonomous terrain classification, averaged-subgradient method, classifier learning, image segmentation, inference task, max flow computation, online self-supervised terrain classification, robot, structured prediction, submodular MRF framework
Paul Vernaza, Ben Taskar, and Daniel D. Lee, "Online, self-supervised terrain classification via discriminatively trained submodular Markov random fields", . May 2008.
Date Posted: 07 October 2009