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A technology of image comparison and alignment method, which is applied in image analysis, image data processing, instruments, etc., and can solve problems such as inability to obtain high-level image content, color features that cannot capture local features well, and lack of mathematical models
Inactive Publication Date: 2011-07-27
XIDIAN UNIV
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The disadvantages of these methods are: the color is not sensitive to changes in the direction and size of the image or image area, and the color feature cannot capture the local characteristics of the object in the image well.
The disadvantages of these methods are: the texture cannot fully reflect the essential properties of the object, and it is impossible to obtain high-level image content only by using texture features.
The disadvantages of these methods are: lack of a complete mathematical model; the comparison results are not reliable when the target is deformed; many shape features only describe the local properties of the target, and it is often time-consuming and expensive to fully describe the target. requirements; the target shape information reflected by many shape features is not completely consistent with human intuitive perception, or in other words, the similarity of the feature space is different from the similarity perceived by the human visual system
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Embodiment 1
[0062] refer to figure 1 , the implementation steps of this example are as follows:
[0063] The first step is to detect the false edge blocks that may exist in the given image and the comparison image, and determine the real area.
[0064] (1.1) Scan the pixel values near the four sides of the given image and compare the statistical distribution. Usually, the distribution of pixel values in normal natural images should be relatively uniform, and no one pixel value will occupy the majority. In the case of proportion, if the occurrence frequency of a certain pixel value is higher than 50%, it is considered that the pixel value is the pixel value of the pseudo edge block caused by rotation, and the pixel connected area near the four edges with the pixel value is considered as a pseudo edge piece;
[0065] (1.2) Take the pixel value of the given image and the pseudo-edge block of the comparison image as the critical threshold, and then perform binarization processing to obt...
Embodiment 2
[0103] refer to figure 2 , the implementation steps of this example are as follows:
[0104] The first four steps are the same as in Embodiment 1.
[0105] The fifth step is to use interval compatibility for comparison
[0106] refer to Figure 4 , the specific implementation of this step is as follows:
[0107] (5a) According to the mean vector of the Zernike moment value of the given image, the standard deviation vector and a set weight α (α>0), an allowable interval P of the i-th Zernike moment value of the given image is obtained:
[0108] P ⊆ [ K ‾ i - α D i , K ‾ i + α D i ] ; i = 1,2 , L , T ;
[0109] T represents the number of...
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Abstract
The invention discloses a zernike moment-based rapid image comparison method which mainly solves the problems that the false judgment rate is high and the arithmetic speed is slow in the prior art. The method is realized by the following steps: (1) a pseudo-side block of an image is detected, and a true area of the image is determined; (2) rotation of the given image is realized for many times, zernike moment, mean vector and standard deviation vector of the image can be calculated after each rotation; (3) normalization processing is carried out to size of the true area of a comparison image, so that the size of the comparison image is as same as the size of the true area of the given image; (4) the zernike moment of the true area of the comparison image after normalization processing is calculated; (5) a similarity rule and an interval compatibility rule are calculated and whether the contents of the given image and the comparison image are the same is judged according to the mean value and the standard deviation vector of the zernike moment of the given image as well as the zernike moment value of the comparison image. The zernike moment-based rapid image comparison method has the advantages of rapidness and high accuracy, and can be applied to occasions such as automatic image comparison or automatic image screening.
Description
technical field [0001] The invention relates to the field of still image processing, in particular to a fast image comparison method based on Zernike moments, which can be used in occasions such as automatic image comparison or automatic image screening. Background technique [0002] With the gradual popularization of the Internet and other networks, there are more and more images on the network. In the face of massive images, how to compare whether two images are the same is a very meaningful problem. The solution to this problem is known as image comparison technology. [0003] Image comparison is the process of comparing a given image and judging whether it is the same or not. The so-called sameness or difference refers to whether the meaningful content in the images we are concerned with is consistent. Some images may look different in size, orientation, etc., but have the same content. However, some images that appear to be the same may not necessarily have the same...
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