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Image texture extraction and identification method by non-Gauss two-dimension Gabor filter

A technology of image texture and recognition method, applied in the field of pattern recognition

Active Publication Date: 2015-09-02
KUNMING UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The invention provides a non-Gaussian two-dimensional Gabor filter image texture extraction and recognition method to solve the problem of non-Gaussian image texture feature extraction and recognition
Aiming at the defect that the local traditional two-dimensional Gabor filter can only extract the Gaussian frequency information of the image texture, the image texture feature extraction method based on the non-Gaussian two-dimensional Gabor filter proposed by the present invention can effectively extract the non-Gaussian texture information of the image

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  • Image texture extraction and identification method by non-Gauss two-dimension Gabor filter

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Embodiment 1

[0031] Embodiment 1: as Figure 1-4 As shown, the non-Gaussian two-dimensional Gabor filter image texture extraction and recognition method first constructs a non-Gaussian two-dimensional Gabor filter, then uses a non-Gaussian two-dimensional Gabor filter to filter the image, and calculates the amplitude of the filtered image matrix, and then divide this magnitude matrix into blocks, calculate the histogram feature vector of each block, and then connect the histogram feature vectors of each block to obtain the histogram feature vector of the original image. Finally, the feature vector is sent to the nearest neighbor classifier based on chi-square statistics for classification and identification.

[0032] The specific steps of the non-Gaussian two-dimensional Gabor filter image texture extraction and recognition method are as follows:

[0033] Step1, first construct a non-Gaussian two-dimensional Gabor filter; the non-Gaussian two-dimensional Gabor filter is constructed as:

...

Embodiment 2

[0045] Embodiment 2: as Figure 1-4 As shown, the non-Gaussian two-dimensional Gabor filter image texture extraction and recognition method first constructs a non-Gaussian two-dimensional Gabor filter, then uses a non-Gaussian two-dimensional Gabor filter to filter the image, and calculates the amplitude of the filtered image matrix, and then divide this magnitude matrix into blocks, calculate the histogram feature vector of each block, and then connect the histogram feature vectors of each block to obtain the histogram feature vector of the original image. Finally, the feature vector is sent to the nearest neighbor classifier based on chi-square statistics for classification and recognition.

[0046] The specific steps of the non-Gaussian two-dimensional Gabor filter image texture extraction and recognition method are as follows:

[0047] Step1, first construct a non-Gaussian two-dimensional Gabor filter; the non-Gaussian two-dimensional Gabor filter is constructed as:

[0...

Embodiment 3

[0062] Embodiment 3: as Figure 1-4 As shown, the non-Gaussian two-dimensional Gabor filter image texture extraction and recognition method first constructs a non-Gaussian two-dimensional Gabor filter, then uses a non-Gaussian two-dimensional Gabor filter to filter the image, and calculates the amplitude of the filtered image matrix, and then divide this magnitude matrix into blocks, calculate the histogram feature vector of each block, and then connect the histogram feature vectors of each block to obtain the histogram feature vector of the original image. Finally, the feature vector is sent to the nearest neighbor classifier based on chi-square statistics for classification and identification.

[0063] The specific steps of the non-Gaussian two-dimensional Gabor filter image texture extraction and recognition method are as follows:

[0064] Step1, first construct a non-Gaussian two-dimensional Gabor filter; the non-Gaussian two-dimensional Gabor filter is constructed as:

...

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Abstract

The invention relates to an image texture extraction and identification method by a non-Gauss two-dimension Gabor filter, and belongs to the mode identification technology field. The method includes the steps of constructing a non-Gauss two-dimension Gabor filter at first, employing the non-Gauss two-dimension Gabor filter to perform filter processing on an image, calculating the amplitude matrix of the filtered image, dividing the amplitude matrix into blocks, calculating the histogram characteristic vector of each block to obtain the histogram characteristic vector of the original image, and finally sending the characteristic vectors into a nearest-neighbor classifier based on chi-squared statistics and classifying and identifying the vectors. The method is a texture description method based on a non-Gauss two-dimension Gabor filter, is a spread way for conventional two-dimension Gabor filters, and can extract and identify non-Gauss texture characteristics of images.

Description

technical field [0001] The invention relates to a non-Gaussian two-dimensional Gabor filter image texture extraction and recognition method, which belongs to the technical field of pattern recognition. Background technique [0002] Two-dimensional Gabor filter is an effective method for image texture feature extraction. The research on two-dimensional Gabor filter mainly focuses on parameter selection of two-dimensional Gabor filter, fast calculation of two-dimensional Gabor filter and various applications of two-dimensional Gabor filter. [0003] The traditional two-dimensional Gabor filter can be regarded as a Gaussian kernel function modulated by a complex sine function in the frequency domain, so the traditional two-dimensional Gabor filter is still Gaussian in the frequency domain, and the traditional two-dimensional Gabor filter is used to After the image is filtered, only the Gaussian frequency information of the image texture can be extracted, and the image texture ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/161G06V10/443G06V10/507G06F18/24147
Inventor 陈熙李闻
Owner KUNMING UNIV OF SCI & TECH
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