Color analysis by learning subspaces and optical processing

Most machine vision systems are based on a three-parameter representation for colors. It is argued that in contrast to three-parameter methods, the whole color spectrum should be used for recognition, resulting in improved accuracy. A method of colour representation based on the spectrum is the subspace method, which is capable of accurate color recognition after a learning phase. The subspace method stems from well-known neural-network models for associative memory. A practical parallel realization for the subspace model is an optical one. Different possibilities for optical implementations are discussed, and concrete color classification results are given.<>