Learning Lightness Algorithms

Anya C. Hurlbert; Tomaso A. Poggio

doi:10.1117/12.960280

27 March 1989 Learning Lightness Algorithms

Anya C. Hurlbert, Tomaso A. Poggio

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Proceedings Volume 1002, Intelligent Robots and Computer Vision VII; (1989) https://doi.org/10.1117/12.960280
Event: 1988 Cambridge Symposium on Advances in Intelligent Robotics Systems, 1988, Boston, MA, United States

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.

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Anya C. Hurlbert and Tomaso A. Poggio "Learning Lightness Algorithms", Proc. SPIE 1002, Intelligent Robots and Computer Vision VII, (27 March 1989); https://doi.org/10.1117/12.960280

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KEYWORDS

Reflectivity

Computer vision technology

Evolutionary algorithms

Machine vision

Robot vision

Robots

Linear filtering

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