Ill-Posed Problems in Early Vision
dc.date.accessioned | 2004-10-01T20:10:30Z | |
dc.date.accessioned | 2018-11-24T10:09:41Z | |
dc.date.available | 2004-10-01T20:10:30Z | |
dc.date.available | 2018-11-24T10:09:41Z | |
dc.date.issued | 1987-05-01 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/5596 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/5596 | |
dc.description.abstract | The 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.extent | 61 p. | en_US |
dc.format.extent | 4456378 bytes | |
dc.format.extent | 3489221 bytes | |
dc.language.iso | en_US | |
dc.subject | computational vision | en_US |
dc.subject | regularization theory | en_US |
dc.subject | sinverse problems | en_US |
dc.subject | ill-posed problems | en_US |
dc.title | Ill-Posed Problems in Early Vision | en_US |
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