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Proceedings Paper

Learning Lightness Algorithms
Author(s): Anya C. Hurlbert; Tomaso A. Poggio
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Paper Abstract

Lightness algorithms, which recover surface reflectance from the image irradiance signal in individual color channels, provide one solution to the computational problem of color constancy. We compare three methods for constructing (or "learning") lightness algorithms from examples in a Mondrian world: optimal linear estimation, backpropagation (BP) on a two-layer network, and optimal polynomial estimation. In each example, the input data (image irradiance) is paired with the desired output (surface reflectance). Optimal linear estimation produces a lightness operator that is approximately equivalent to a center-surround, or bandpass, filter and which resembles a new lightness algorithm recently proposed by Land. This technique is based on the assumption that the operator that transforms input into output is linear, which is true for a certain class of early vision algorithms that may therefore be synthesized in a similar way from examples. Although the backpropagation net performs slightly better on new input data than the estimated linear operator, the optimal polynomial operator of order two performs marginally better than both.

Paper Details

Date Published: 27 March 1989
PDF: 8 pages
Proc. SPIE 1002, Intelligent Robots and Computer Vision VII, (27 March 1989); doi: 10.1117/12.960280
Show Author Affiliations
Anya C. Hurlbert, Massachusetts Institute of Technology (United States)
Tomaso A. Poggio, Massachusetts Institute of Technology (United States)

Published in SPIE Proceedings Vol. 1002:
Intelligent Robots and Computer Vision VII
David P. Casasent, Editor(s)

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