Generalized smoothing networks in early vision

Generalized smoothing networks have been developed which enforce smoothness constraints for any arbitrary level of derivative of the input data. Furthermore, discontinuities of any order of derivative can be detected by providing for continuous line processes, which selectively inhibit smoothing. Second- and higher-order networks are required for many problems in early vision; first-order networks are often unsatisfactory. Examples in surface interpolation, edge detection, and image segmentation are shown. Solution of these types of problems typically takes a prohibitive amount of time, even on supercomputers. A significant advantage of these proposed networks is that they can be mapped directly to analog VLSI hardware.<
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