| dc.creator | Hurlbert, Anya | |
| dc.creator | Poggio, Tomaso | |
| dc.date | 2004-10-01T20:10:35Z | |
| dc.date | 2004-10-01T20:10:35Z | |
| dc.date | 1987-06-01 | |
| dc.date.accessioned | 2013-10-09T02:40:15Z | |
| dc.date.available | 2013-10-09T02:40:15Z | |
| dc.date.issued | 2013-10-09 | |
| dc.identifier | AIM-909 | |
| dc.identifier | http://hdl.handle.net/1721.1/5601 | |
| dc.identifier.uri | http://koha.mediu.edu.my:8181/xmlui/handle/1721 | |
| dc.description | 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. | |
| dc.format | 30 p. | |
| dc.format | 4549310 bytes | |
| dc.format | 1641242 bytes | |
| dc.format | application/postscript | |
| dc.format | application/pdf | |
| dc.language | en_US | |
| dc.relation | AIM-909 | |
| dc.subject | computer vision | |
| dc.subject | color constancy | |
| dc.subject | learning | |
| dc.subject | regularization | |
| dc.subject | soptimal estimation | |
| dc.subject | pseudoinverse | |
| dc.title | Learning a Color Algorithm from Examples |
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