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Color constancy calculating method and system based on derivative structure of image

A technology of color constancy and calculation system, applied in the field of color constancy calculation based on image derivative structure, can solve problems such as high cost ratio, slow training speed of BP neural network, and insufficient use of image edge structure, etc., to improve performance , the effect of fewer parameters

Inactive Publication Date: 2010-05-12
BEIJING JIAOTONG UNIV
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Problems solved by technology

[0007] (1) The supervised color constancy algorithms in the prior art all use the binarized chromaticity histogram of the original image to form the feature vector, and do not make full use of information such as the edge structure of the image
[0008] (2) The training speed of BP neural network is very slow, and it is easy to fall into local optimum; while the selection of kernel function and its parameters based on SVR method is a complicated problem, because there are multiple kernel functions, and for each kernel The function has multiple parameters. Therefore, how to choose the optimal kernel function and its corresponding parameter pair can only be done through experience and trial methods, and the implementation cost is relatively high.

Method used

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  • Color constancy calculating method and system based on derivative structure of image
  • Color constancy calculating method and system based on derivative structure of image
  • Color constancy calculating method and system based on derivative structure of image

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Embodiment 1

[0031] figure 1 shows the overall algorithm framework of the present invention, such as figure 1 As shown, the color constancy calculation method based on the image derivative structure includes the following steps:

[0032] (1) Feature extraction based on image derivative structure

[0033] The feature extraction based on the image derivative structure is the key step of this algorithm. For the input image f, its first derivative image is calculated separately and the second derivative image In order to further eliminate the influence of noise derivation, this embodiment uses and replace and in, Represents an image f with a Gaussian filter G σ The convolution, as attached figure 1 shown.

[0034] Then, calculate the chromaticity histogram for the original image, the first-order derivative image and the second-order derivative image respectively. Convert the original RGB color space to rg chromaticity space, the conversion formula is as follows:

[0035] ...

Embodiment 2

[0046] Such as image 3 As shown, in this embodiment, the color constancy calculation system based on the image derivative structure includes the following modules:

[0047] An image feature extraction module, which extracts image features based on the image derivative structure, and proposes a chromaticity histogram feature vector;

[0048] The neural network training and learning module uses the chromaticity histogram feature vector extracted by the image feature extraction module as the input vector of the neural network training module, uses the triple cross validation method to set the number of neurons in the hidden layer for the neural network, and uses the neural network The network is trained, and the illumination chromaticity corresponding to each training image constitutes the output vector output of the neural network; and for the image to be tested, first calculate its fused chromaticity histogram feature vector, and input it to the trained neural network , get t...

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Abstract

The invention relates to a color constancy calculating method and a system based on a derivative structure of an image, belonging to the technical field of color constancy calculation and image light treatment. The color constancy calculating method comprises the steps: firstly, extracting image features based on the derivative structure of the image, providing a feature vector of a chromaticity histogram, so as to be used for calculating the color constancy; secondly, training the feature vector of the chromaticity histogram by applying an ELM neural network, using the illumination chromaticity corresponding to every training image to constitute an output vector of the neural network; and finally, carrying out illumination correction to the tested image by an opposite angle model, so as to obtain image color under white light. The invention fully utilizes the feature information of the derivative structure of the image to improve the calculation performance of the color constancy, and has the advantages of high learning speed, strong generalization capability, and capability of simultaneously carrying out the evaluation of two-dimensional illumination chromaticity, and the like.

Description

technical field [0001] The invention belongs to the technical field of color constancy calculation and image illumination processing, and in particular relates to a color constancy calculation method based on an image derivative structure. Background technique [0002] As a simple, direct and effective feature, color has been widely used in various computer vision-related fields such as object recognition, image retrieval, and scene understanding. However, color is an extremely unstable image feature. The image color obtained by any imaging device depends on at least three main factors: the physical reflection characteristics of the object surface in the scene, the lighting conditions in the scene during imaging, and the imaging conditions. The imaging parameters of the device, therefore, there may be huge differences in the image color of the same scene under different lighting. Fortunately, the color constancy function of the human visual system can well eliminate the inf...

Claims

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Application Information

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IPC IPC(8): G06T11/00G06N3/02G06N3/08
Inventor 李兵郎丛妍须德
Owner BEIJING JIAOTONG UNIV
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