title: Synthesizing a color algorithm from examples.
abstract: A lightness algorithm that separates surface reflectance from illumination in a Mondrian world is synthesized automatically from a set of examples, which consist of pairs of input (intensity signal) and desired output (surface reflectance) images. The algorithm, which resembles a new lightness algorithm recently proposed by Land, is approximately equivalent to filtering the image through a center-surround receptive field in individual chromatic channels. The synthesizing technique, optimal linear estimation, requires only one assumption, that the operator that transforms input into output is linear. This assumption is true for a certain class of early vision algorithms that may therefore be synthesized in a similar way from examples. Other methods of synthesizing algorithms from examples, or "learning," such as back-propagation, do not yield a significantly better lightness algorithm.
authors.affiliation: Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge 02139.
authors.forename: A C
authors.initials: AC
authors.lastname: Hurlbert
authors.forename: T A
authors.initials: TA
authors.lastname: Poggio
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003118:Color Perception; D006801:Humans; D007858:Learning; D008027:Light