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Modern probabilistic modeling has revolutionized the design and implementation of machine vision systems. There are now numerous instances of systems that can see stereoscopically in depth, or separate foreground from background, or accurately excise objects of a particular class, all in real time. Each of those three vision functionalities will be demonstrated in the lecture. The underlying advances in system design and performance owe much to probabilistic frameworks for inference in images. In particular, the Markov Random Field (MRF), which first appeared in image processing in the 70s, has staged a resounding comeback in the last decade. The MRF is a mechanism, borrowed from statistical physics, for expressing prior properties of images, such as smoothness and spatial coherence. Despite its considerable generality, the MRF has proved nonetheless to be remarkably tractable when used in inference systems, as the lecture will explain.