Learning a Color Algorithm from Examples
dc.date.accessioned | 2004-10-01T20:10:35Z | |
dc.date.accessioned | 2018-11-24T10:09:42Z | |
dc.date.available | 2004-10-01T20:10:35Z | |
dc.date.available | 2018-11-24T10:09:42Z | |
dc.date.issued | 1987-06-01 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/5601 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/5601 | |
dc.description.abstract | We show that a color algorithm capable of separating illumination from reflectance in a Mondrian world can be learned from a set of examples. The learned algorithm is equivalent to filtering the image data---in which reflectance and illumination are mixed---through a center-surround receptive field in individual chromatic channels. The operation resembles the "retinex" algorithm recently proposed by Edwin Land. This result is a specific instance of our earlier results that a standard regularization algorithm can be learned from examples. It illustrates that the natural constraints needed to solve a problemsin inverse optics can be extracted directly from a sufficient set of input data and the corresponding solutions. The learning procedure has been implemented as a parallel algorithm on the Connection Machine System. | en_US |
dc.format.extent | 30 p. | en_US |
dc.format.extent | 4549310 bytes | |
dc.format.extent | 1641242 bytes | |
dc.language.iso | en_US | |
dc.subject | computer vision | en_US |
dc.subject | color constancy | en_US |
dc.subject | learning | en_US |
dc.subject | regularization | en_US |
dc.subject | soptimal estimation | en_US |
dc.subject | pseudoinverse | en_US |
dc.title | Learning a Color Algorithm from Examples | en_US |
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