Method for illuminating/normalizing image and method for identifying image by using same
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A normalization and image technology, applied in the field of pattern recognition, to achieve the effect of enhancing effectiveness and overcoming the influence of lighting factors
Active Publication Date: 2010-12-15
BEIJING UNIV OF TECH
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[0013] The technical solution of the present invention is to overcome the influence of illumination factors in the input image, provide a method for normalizing the
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Embodiment 1
[0046] The illumination normalization in this embodiment is improved on the basis of the TVQI model.
[0047] Such as figure 1 As shown, the method for performing illumination normalization processing on an image includes the following steps:
[0048] (1) Normalize the input image to a 100×100 grayscale image I
[0049] If the input image is a grayscale image, modify the image pixels to 100×100;
[0050] If the input image is a color image, the color image is converted into a grayscale image by the following formula:
[0051] gray = r + g + b 3
[0052] In the formula, r, g, and b represent the values of the red, green, and blue components in the color image, respectively, and gray represents the gray value of the grayscale image.
[0053] (2) Segment the input image into shadow areas and normal light areas
[0054] The method of dividing the inp...
Embodiment 2
[0139] The illumination normalization in this embodiment is improved on the basis of the LTV model.
[0140] LTV model comes from: "Total variation models for variable lighting face recognition" Chen, Terrence; Yin, Wotao; Zhou, Xiang Sean; Comaniciu, Dorin; Huang, Thomas S. Source: IEEE Transactions on Pattern Analysis and Machine Intelligence, v 28, n 9 , p 1519-1524, September 2006.
[0141] The LTV model and the TVQI model come from the same article. The difference between the two is that TVQI uses a quotient operation, and LTV uses a logarithmic operation. Using the LTV model differs from using the TVQI model only in steps (3) and (4).
[0142] Only the differences from Embodiment 1 will be described below.
[0143] Step 3: Use the LTV model to process the normalized input image obtained in step 1 to obtain λ L1 = 0.2, the small-scale part v of the face image λ＝0.2 . The specific steps are:
[0144] A. Perform logarithmic operations on the input image
[0145] f(x,...
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Abstract
The invention relates to a method for illuminating/normalizing an image and a method for identifying an image by using the same, wherein the method for illuminating/normalizing the image comprises the steps of: dividing the input image into a shadow zone and a normal illumination zone; obtaining the small scale part of the image when lambda L1 is valued within 0 to 0.5 according to a TVQI model or LTV model, preferably lambda L1=0.2; obtaining the small scale part of the image when the lambda L1 is valued within 0.6 to 1, preferably lambda L1=0.8; taking the small scale part of the image obtained when the lambda L1 is valued within 0 to 0.5 (preferably 0.2) as the small scale part of the shadow zone of the image, then taking the small scale part of the image obtained when lambda L1 is valued within 0.6 to 1 (preferably 0.8) as the small scale part of the normal illumination zone of the image, and splicing to obtain the small scale part v of the whole image. The invention can enhance the effectiveness of image identification under a complicated illumination condition without knowing the light source information previously.
Description
technical field [0001] The invention relates to a method for performing illumination normalization processing on an image, and also relates to an image recognition method using the same, which belongs to the field of pattern recognition. Background technique [0002] In recent years, face recognition research has received extensive attention. The three major issues of illumination, posture and expression have always been important factors affecting the accuracy of face recognition. Among them, the illumination factor, especially the change of natural ambient light, cannot be controlled by humans. Therefore, illumination processing is a necessary step for every face recognition system. . Most face recognition systems usually have certain restrictions on lighting conditions. Assuming that the image to be processed is obtained under basically uniform lighting conditions, they only allow changes in lighting conditions in a small range. However, the actual lighting conditions ar...
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