We will compare three different approaches to low-level motion analysis: 1) gradient techniques based on spatial and temporal derivatives (e.g., Horn and Schunk); 2) spatio-temporal energy methods, based on tuned filters (e.g., Adelson and Bergen); and 3) regression methods, which find a best-fitting plane in spatio-temporal frequency (e.g., Heeger). Each of these has, at some point, been proposed as a model for low-level motion processing in human vision. We will demonstrate that these approaches, although based on different assumptions, are very closely related. In fact, when they are formulated as velocity estimators, and when the parameters of each model are suitably chosen, the three methods are computationally equivalent. Thus, it is difficult to experimentally determine their relative validity as models of human visual processing. Furthermore, in their equivalent form, all three techniques extract only a single motion vector for each spatial position in the visual field, and so they are incapable of representing the multiple motions that occur near occlusion boundaries and in situations of transparent motion. We suggest extensions of these approaches which can handle these cases. Deprecated: stripslashes(): Passing null to parameter #1 ($string) of type string is deprecated in /System/Volumes/Data/e/1.3/p1/lcv/html_public/pubs/utils.php on line 171
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