Proceedings PaperRobust computational vision
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This paper presents a paradigm for formulating reliable machine vision algorithms using methods from robust statistics. Machine vision is the process of estimating features from images by fitting a model to visual data. Vision research has produced an understanding of the physics and mathematics of visual processes. The fact that computer graphics programs can produce realistic renderings of artificial scenes indicates that our understanding of vision processes must be quite good. The premise of this paper is that the problem in applying computer vision in realistic scenes is not the fault of the theory of vision. We have good models for visual phenomena, but can do a better job of applying the models to images. Our understanding of vision must be used in computations that are robust to the kinds of errors that occur in visual signals. This paper argues that vision algorithms should be formulated using methods from robust regression. The nature of errors in visual signals is discussed, and a prescription for formulating robust algorithms is described. To illustrate the concepts, robust methods have been applied to several problems: surface reconstruction, dynamic stereo, image flow estimation, and edge detection.