Method of estimating chromaticity of illumination using neural n

Details Method of estimating chromaticity of illumination using neural n Method of estimating chromaticity of illumination using neural n

1996-11-15

1999-05-25

Boudreau, Leo H.

Image analysis

Color image processing

382156, 382157, G06K 900, G06K 962

Patent

active

059076291

ABSTRACT:
A method of estimating the chromaticity of illumination of a colored image consisting of a plurality of color-encoded pixels. The image colors are first mapped into an intensity-independent chromaticity space which is then divided into a plurality of separate regions. For each region, a first binary value is assigned to the region if the region contains no chromaticity value; or, a second binary value is assigned to the region if it does contain a chromaticity value. The assigned values are then applied as inputs to a pre-trained neural network having two output ports and at least one intermediate layer containing a plurality rality of ports connectible between selected input ports and the output ports. The chromaticity space values which characterize the input image's chromaticity of illumination are then derived at the output ports. The network is pretrained trained by initially connecting an arbitrary number of the intermediate layer ports to selected input layer ports. A weight value is associated with each connection. The weight values, which have the effect of altering signals transmitted along each connection by a selected amount, are initialized with random values. Each one of a plurality of pre-stored data sets, each containing values characterizing presence or absence of color in selected regions of one of a corresponding plurality of known colored images, are sequentially presented as inputs to the network and the chromaticity space values derived at the output ports are compared with known chromaticity space values characterizing illumination of the known colored image to derive an error value representative of difference therebetween. The weight values are adjusted in response to the inputs in accordance with the well known back propagation algorithm. After the weights are adjusted the intermediate layer ports are adaptively reconnected to the input layer ports to eliminate connections to input layer ports which repeatedly receive zero value inputs. The training process continues until the error value is less than a selected threshold.

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