Method of estimating chromaticity of illumination using neural networks
First Claim
1. A method of estimating the chromaticity of illumination of a colored image consisting of a plurality of color-encoded pixels which define a corresponding plurality of image colors, said method comprising the steps of:
- a. mapping said image colors into an intensity-independent chromaticity space;
b. dividing said chromaticity space into a plurality of separate regions;
c. for each one of said regions;
i. assigning a first value to said region if said region contains no chromaticity value corresponding to any of said image colors;
ii. assigning a second value to said region if said region contains a chromaticity value corresponding to any of said image colors;
d. applying each one of said assigned values to a different one of a plurality of input ports in an input layer of a pre-trained neural network, said neural network having;
i. an output layer containing two output ports;
ii. at least one intermediate layer containing a plurality of ports connectible between selected ports in layers adjacent to said intermediate layer; and
,e. reading, at said respective output ports, chromaticity space values which characterize said chromaticity of illumination.
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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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Citations
13 Claims
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1. A method of estimating the chromaticity of illumination of a colored image consisting of a plurality of color-encoded pixels which define a corresponding plurality of image colors, said method comprising the steps of:
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a. mapping said image colors into an intensity-independent chromaticity space; b. dividing said chromaticity space into a plurality of separate regions; c. for each one of said regions; i. assigning a first value to said region if said region contains no chromaticity value corresponding to any of said image colors; ii. assigning a second value to said region if said region contains a chromaticity value corresponding to any of said image colors; d. applying each one of said assigned values to a different one of a plurality of input ports in an input layer of a pre-trained neural network, said neural network having; i. an output layer containing two output ports; ii. at least one intermediate layer containing a plurality of ports connectible between selected ports in layers adjacent to said intermediate layer; and
,e. reading, at said respective output ports, chromaticity space values which characterize said chromaticity of illumination. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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Specification