An Image Representation Method and Its Application in Image Matching and Recognition
An image representation and image technology, applied in the field of image processing, can solve the problems of large differences in description effects and poor feature robustness, and achieve the effect of improving the accuracy of quantity and matching, improving the accuracy of matching, and enriching image features
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Embodiment 1
[0052] Embodiment 1: Image representation method based on phase singularity
[0053] Such as figure 1 As shown, the flow of the image representation method of this application is as follows:
[0054] S1-1 Read image information
[0055] In this step, the two-dimensional image equation I (x, y) is obtained by reading the image information;
[0056] S1-2 Two-dimensional complex filter processing
[0057] Combine the two-dimensional image equation I(x, y) with the two-dimensional complex filter T(x, y)=T r (x,y)+iT l (x, y) is convolved to obtain a complex image:
[0058]
[0059] Among them, * is the convolution calculation symbol, (x, y) indicates the position of the image pixel.
[0060] The two-dimensional complex filter used in this embodiment is a Laguerre-Gaussian (LG) complex filter:
[0061]
[0062] Tr(x,y)=Re{LG(x,y,σ)}=G x (x, y, σ)
[0063]Ti(x,y)=Im{LG(x,y,σ)}=G y (x, y, σ)
[0064] In the formula, σ is the scale parameter factor. Two-dimensional co...
Embodiment 2
[0104] Embodiment 2: Image matching based on phase singularity
[0105] S2-1 read image I and image I' information
[0106] Read the two-dimensional image equations I(x, y) and I'(x, y) of image I and image I' respectively;
[0107] S2-2 Perform two-dimensional complex filter processing on image I and image I'
[0108] I (x, y) and I' (x, y) are carried out respectively according to the method of S1-2 in the embodiment 1 complex number filtering process, obtain:
[0109] The complex image equation of image I is
[0110]
[0111]
[0112] The complex image equation of image I' is
[0113] ;
[0114]
[0115] S2-3 Phase singular point detection of image I and image I'
[0116] According to the method for S1-3 phase singularity detection among the embodiment 1, image I and image I ' are carried out phase singularity detection, the collection of the phase singularity detected in image I is {P i}, the set of phase singularities detected in image I’ is {P i '};
...
Embodiment 3
[0135] Embodiment 3: Image recognition based on phase singularity
[0136] S3-1 According to the category attributes of the samples, use the SVM classifier to perform classifier training for different types of samples, and obtain classifiers for different types of images
[0137] Creation of S3-1-1 samples
[0138] The training samples are divided into positive samples and negative samples, where the positive sample is the target sample to be detected, such as training a car image classifier, the positive sample is a car; the negative sample refers to any other picture other than the positive sample picture .
[0139] S3-1-2 Training classifier
[0140] According to the method in embodiment 1, all the pictures in the sample are detected and positioned at the phase singularity;
[0141] According to the method in embodiment 1, descriptors are generated for all phase singularities extracted;
[0142] Selection of kernel function:
[0143] A kernel function is selected to de...
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