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Ill-Posed Problems in Early Vision

dc.date.accessioned2004-10-01T20:10:30Z
dc.date.accessioned2018-11-24T10:09:41Z
dc.date.available2004-10-01T20:10:30Z
dc.date.available2018-11-24T10:09:41Z
dc.date.issued1987-05-01en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/5596
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/5596
dc.description.abstractThe first processing stage in computational vision, also called early vision, consists in decoding 2D images in terms of properties of 3D surfaces. Early vision includes problems such as the recovery of motion and optical flow, shape from shading, surface interpolation, and edge detection. These are inverse problems, which are often ill-posed or ill-conditioned. We review here the relevant mathematical results on ill-posed and ill-conditioned problems and introduce the formal aspects of regularization theory in the linear and non-linear case. More general stochastic regularization methods are also introduced. Specific topics in early vision and their regularization are then analyzed rigorously, characterizing existence, uniqueness, and stability of solutions.en_US
dc.format.extent61 p.en_US
dc.format.extent4456378 bytes
dc.format.extent3489221 bytes
dc.language.isoen_US
dc.subjectcomputational visionen_US
dc.subjectregularization theoryen_US
dc.subjectsinverse problemsen_US
dc.subjectill-posed problemsen_US
dc.titleIll-Posed Problems in Early Visionen_US


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