CN114724188B - Vein identification method and device based on gray level co-occurrence matrix - Google Patents
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Abstract
The invention discloses a vein identification method and a vein identification device based on a gray level co-occurrence matrix, which belong to the technical field of vein identification and processing and comprise the following steps: 1) carrying out scale normalization pretreatment on the vein image; 2) filtering the vein image after the normalization pretreatment by using homomorphic filtering; 3) enhancing the filtered vein image by using a local contrast enhancement method; 4) and identifying the enhanced vein image based on the gray level co-occurrence matrix. According to the invention, the gray level co-occurrence matrix is introduced to carry out vein image matching identification, so that the calculated amount is reduced, and the image identification speed is obviously improved; the gray level correction method based on the combination of the improved homomorphic filtering and the local contrast enhancement improves the influence of the illumination nonuniformity on the vein image, not only can keep the low-frequency component, but also can enhance the high-frequency component, removes redundant noise, enhances the dark details of the vein image, and improves the recognition rate of vein recognition.
Description
Technical Field
The invention relates to the technical field of vein identification and processing, in particular to a vein identification method and device based on a gray level co-occurrence matrix.
Background
Vein recognition is a new infrared biological recognition technology, which is to use an infrared camera to shoot the distribution diagram of veins in vivo (back of the hand, back of the finger, abdomen of the finger, palm and wrist) according to the characteristic that hemoglobin in the blood of veins in the human body absorbs near infrared or radiates far infrared rays of the human body.
The gray level correction is an image detail enhancement method, is used for enhancing a magnetic resonance image in the early stage, has a good enhancement effect on the image quality, and is also applied to vein recognition at present to realize detail enhancement of the vein image. For example, chinese patent CN112560808B discloses a method and a device for in vivo vein recognition based on gray scale information, wherein the vein recognition method includes the following steps: 1) carrying out scale normalization pretreatment on the in-vivo vein image; 2) adopting a gray level correction method combining regional variance transformation and single-scale Retinex to perform enhancement processing on the vein image after normalization processing; 3) carrying out rough image matching based on an improved gray difference curved surface method; 4) and carrying out fine image matching based on a correlation coefficient method.
When the vein image is enhanced by the vein identification, the vein image is filtered by a Gaussian filter, and after the vein image is filtered by the traditional Gaussian filter, a lot of low-frequency information is lost in the image, so that a smooth area of the image basically disappears, and the identification rate is influenced; when the filtered vein image is subjected to enhancement processing, some high-frequency parts may have an over-enhancement phenomenon, so that noise enhancement is caused, and the feature extraction is inaccurate; in addition, the conventional vein recognition method has a problem of large calculation amount, and the image recognition speed is affected.
Disclosure of Invention
The invention aims to provide a vein identification method and device based on a gray level co-occurrence matrix, and aims to solve the problems that in the prior art, the vein image enhancement effect is poor, and the identification effect is poor due to inaccurate feature extraction.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
the invention relates to a vein identification method based on a gray level co-occurrence matrix, which comprises the following steps:
1) carrying out scale normalization pretreatment on the vein image;
2) filtering the vein image after the normalization pretreatment by using homomorphic filtering;
3) enhancing the filtered vein image by using a local contrast enhancement method;
4) and identifying the enhanced vein image based on the gray level co-occurrence matrix.
Preferably, in the step 1), a bilinear interpolation method is adopted to perform scale normalization processing on the vein image.
Preferably, the specific way of performing the filtering processing on the vein image after the normalization preprocessing by using the homomorphic filtering in the step 2) is as follows:
2.1) vein image after scale normalization processingCarrying out logarithmic transformation, and leading:
in the formula,is a vein image after the logarithmic transformation,xandyrespectively a row coordinate and a column coordinate of the vein image matrix;
2.2) taking Fourier transform on two sides of the above formula, and enabling:
in the formula,for the logarithmically transformed vein imageThe fourier transformed image of (a) is,uandvrespectively x and y are variables obtained by Fourier transform,is Fourier transform;
2.3) passing Gaussian filterFiltering the above formula to obtain a filtered frequency domain imageThe calculation formula is as follows:
in the formula:is a high-frequency amplification factor, has a value range of more than 1 and less than 2,the offset is a parameter with the value range of more than 0 and less than 1 and controlling the slope of the filterIs the center of the filter and is,is the mid-point in the frequency domain, is the high frequency gain,in order to gain at a low frequency, the gain is,cfor the sharpening constant, the value range is;
2.4) carrying out inverse Fourier transform on the filtered vein image to obtain an image with a frequency domain converted into a space domainThat is, the final filtered image is obtained, and the formula of the inverse fourier transform is:
Preferably, the step 3) of performing enhancement processing on the filtered vein image by using a local contrast enhancement method specifically includes:
3.1) filtering the processed imagePerforming exponential operation to obtain homomorphic filtered image:
3.2) settingIs an imageThe gray value of a certain point in (b),iandjrespectively being said imagesRow and column coordinates of the matrix, andas a center, the window size is (2)n+1)*(2nThe region of +1) is a local region, whereinnCalculating a local average value for a positive integerAnd local variance,
the calculation formula of the local variance is as follows:
in the formula,is the average of the local areas and is,the local standard deviation is taken as the local standard deviation,aandbrespectively are horizontal and vertical coordinates in a local area;
3.3) is provided withIs composed ofCorresponding enhanced pixel value, enhanced pixelThe calculation formula of the value is:
then the
In the formula,Cis a constant value with a value range of more than 0 and less than 1,is the global mean square error.
Preferably, the step 4) of identifying the enhanced vein image based on the gray level co-occurrence matrix specifically includes:
4.1) carrying out feature compression on the image;
4.2) solving the gray level co-occurrence matrixGray level co-occurrence matrixIs shown asIn the direction and at a distanceHas a gray value respectivelyAndprobability of occurrence, then pixel pairAndgray level co-occurrence matrix in four directionsThe calculation formula of (2) is as follows:
in the formula,is a pointIs determined by the gray-scale value of (a),is a pointIs determined by the gray-scale value of (a),Lis a gray scale level of the image,andrepresenting the number of lines and rows of the vein image;
4.3) co-occurrence matrix according to gray levelTaking a distanceIs 1, angle0 degree, 45 degrees, 90 degrees and 135 degrees respectively, and then the gray level co-occurrence matrix is alignedAnd (4) carrying out normalization, wherein the calculation formula is as follows:
in the formula,in order to normalize the co-occurrence matrix,for the normalized constant, the calculation formula is:
in the formula,Mis the size of the co-occurrence matrix, typically 8, 16, 32;
4.4) obtaining each texture characteristic, namely, the gray level co-occurrence matrix4 texture parameters such as energy, entropy, moment of inertia and correlation are calculated, wherein the energy ASMThe calculation formula is as follows:
in the formula,is the mean value of the gray levels,in order to smooth the average value of the average,as a standard deviation of the gray scale,in order to smooth out the standard deviation of the standard deviation,、、、the calculation methods of (A) are respectively as follows:
connecting the mean values and standard deviations of the energy, entropy, moment of inertia and correlation 4 texture parameters on four angles in series to serve as a template characteristic matrix of the image;
4.5) comparing the characteristics, namely subtracting the characteristic matrix to be matched from the characteristic matrix of the template to obtain a difference matrix A, and then calculating the Euclidean norm of the matrix A, wherein the norm of the matrix A can be realized by solving the maximum singular value of the matrix, and the calculation formula is as follows:
in the formula,representing the singular value of the difference matrix A, and T represents the maximum singular value;
4.6) selecting a threshold value by using the size of the maximum singular value T, and carrying out image identification according to the threshold value.
Preferably, the characteristic compression of the image in the step 4.1) is to compress the gray level of the original image, i.e. to quantize the gray level 256 to 16 levels and convert the gray level 0-255 to the gray level 0-15.
The invention also relates to a vein recognition device based on the gray level co-occurrence matrix, which comprises the following components:
1) the normalization preprocessing module is used for carrying out scale normalization preprocessing on the vein image;
2) the filtering processing module is used for carrying out filtering processing on the vein image after the normalization preprocessing by using homomorphic filtering;
3) the enhancement processing module is used for enhancing the filtered vein image by using a local contrast enhancement method;
4) and the image identification module is used for identifying the enhanced vein image based on the gray level co-occurrence matrix.
Compared with the prior art, the technical scheme provided by the invention has the following beneficial effects:
1. the vein identification method based on the gray level co-occurrence matrix introduces the gray level co-occurrence matrix to carry out vein image matching identification, in the identification process, firstly, the image is subjected to feature compression, the gray level co-occurrence matrix of the compressed image is calculated, then, the mean value and the standard deviation of 4 texture parameters are connected in series to serve as the feature matrix of the image, then, the template feature and the feature to be matched are subtracted, then, the maximum singular value of the template feature is calculated, finally, the appropriate threshold value is selected by utilizing the size of the maximum singular value, whether veins belong to the same class or not is judged according to the threshold value, the calculated amount is reduced, and the image identification speed is obviously improved.
2. The vein identification method based on the gray level co-occurrence matrix provides a new gray level correction calculation method for carrying out filtering processing on the vein image, namely the filtering processing is mainly carried out based on improved homomorphic filtering.
3. The vein identification method based on the gray level co-occurrence matrix provided by the invention can avoid the oscillation generated at the position where the gray level of the output image is changed violently, avoid the enhancement of noise, and further contribute to improving the identification rate of vein identification.
Drawings
FIG. 1 is a flow chart of a vein identification method based on a gray level co-occurrence matrix;
fig. 2 is a structural framework diagram of vein recognition based on a gray level co-occurrence matrix.
Detailed Description
For further understanding of the present invention, the present invention will be described in detail with reference to examples, which are provided for illustration of the present invention but are not intended to limit the scope of the present invention.
Example 1
Referring to fig. 1, the present invention relates to a vein identification method based on gray level co-occurrence matrix, which comprises the following steps:
1) carrying out scale normalization processing on the vein image by adopting a bilinear interpolation method;
2) filtering the vein image after the normalization pretreatment by using homomorphic filtering, which comprises the following specific steps:
in the formula,is a vein image after the logarithmic transformation,xandyrespectively a row coordinate and a column coordinate of the vein image matrix;
2.2) taking Fourier transform on two sides of the above formula, and enabling:
in the formula,is composed ofThe fourier transformed image of (a) is,uandvare respectively asx,yThe variables obtained by the Fourier transform are used as the variables,is Fourier transform;
2.3) passing Gaussian filterFiltering the above formula to obtain a filtered frequency domain imageThe calculation formula is as follows:
in the formula:c is a constant value for the cut-off frequency,andrespectively a high-frequency and a low-frequency gain,is a pointTo the origin of the frequency planeThe calculation formula of (c) is:
since the image after the traditional gaussian high-pass filtering loses much low-frequency information, the smooth area of the image basically disappears, and in order to adapt to the vein image, the gaussian filter in the embodimentThe calculation formula of (2) is as follows:
in the formula:is a high-frequency amplification factor, has a value range of more than 1 and less than 2,the offset is a parameter with the value range of more than 0 and less than 1 and controlling the slope of the filterIs the center of the filter and is,is the mid-point in the frequency domain,in order to gain the gain at high frequency,in order to gain the gain at low frequencies,cfor the sharpening constant, the value range is;
Compared with the traditional Gaussian filter, after the filtering processing is carried out by adopting the calculation method of the Gaussian filter, not only can the low-frequency component be reserved, but also the high-frequency component can be enhanced, so that the details of the dark part of the vein image are enhanced;
2.4) carrying out inverse Fourier transform on the filtered vein image to obtain an image with a frequency domain converted into a space domainThat is, the final filtered image is obtained, and the formula of the inverse fourier transform is as follows:
3) the method for enhancing the vein image after the filtering processing by using the local contrast enhancement method comprises the following specific steps:
3.1) filtering the processed imagePerforming exponential operation to obtain homomorphic filtered image:
3.2) settingIs an imageThe gray value of a certain point in (b),iandjrespectively being said imagesRow and column coordinates of the matrix toAs a center, the window size is (2)n+1)*(2nThe region of +1) is a local region, whereinnCalculating a local average value for a positive integerAnd local variance,
the calculation formula of the local variance is as follows:
in the formula,is the average of the local areas and is,the local standard deviation is taken as the local standard deviation,aandbrespectively are horizontal and vertical coordinates in a local area;
3.3) is provided withIs composed ofAnd corresponding to the enhanced pixel value, wherein the calculation formula of the enhanced pixel value is as follows:
in the formula,is a gain constant, and only needs to satisfy the requirement when adopting the traditional calculation methodIn order to solve the problem that some high frequency parts may be over-enhanced, the embodiment further comprises:
then
In the formula,Cis a constant value with a value range of more than 0 and less than 1,is the global mean square error;
because the improved contrast gain mu is spatially adaptive and inversely proportional to the local mean square error gamma _ f (i, j), the local mean square error is larger at the edge of the vein image or other places with severe changes, so the gain is smaller, and the oscillation generated at the places with severe changes of the gray scale of the output image can be avoided. In a smooth region, the local mean square error is small, so that the value of μ is large, and the constant C and the global mean square error δ are used for controlling the degree of high-frequency enhancement to avoid causing noise enhancement.
4) Recognizing the enhanced vein image based on the gray level co-occurrence matrix, and specifically comprising the following steps of:
4.1) performing characteristic compression on the image, namely compressing the gray level of the original image to reduce the calculated amount, and quantizing the gray level into 16 levels at 256 levels. Since the vein image gray scale is generally distributed in a narrow range, if the gray scale of the original vein image is directly compressed to 16 levels, the image definition is reduced. However, in step 2), the local contrast enhancement is already performed on the image, and the dynamic range of the gray value is increased, so that the overall contrast effect of the image is increased. So here the enhanced vein image gray scale can be directly divided by 16 and rounded, converting 0-255 gray scale to 0-15 gray scale;
4.2) solving the gray level co-occurrence matrixGray level co-occurrence matrixIs shown asIn the direction and at a distanceRespectively having a gray valueAndprobability of occurrence, then pixel pairAndgray level co-occurrence matrix in four directionsThe calculation formula of (c) is:
in the formula,is a pointIs determined by the gray-scale value of (a),is a pointIs measured in a predetermined time period, and the gray value of (b),Lis a gray scale level of the image,andrepresenting the number of lines and rows of the vein image;
4.3) co-occurrence matrix according to gray levelTaking a distanceIs an angle of 10 degree, 45 degrees, 90 degrees and 135 degrees respectively, and then the gray level co-occurrence matrix is alignedAnd (4) carrying out normalization, wherein the calculation formula is as follows:
in the formula,in order to normalize the co-occurrence matrix,for the normalized constants, the calculation formula is:
where M is the size of the co-occurrence matrix, typically 8, 16, 32.
4.4) obtaining each texture characteristic, namely, the gray level co-occurrence matrix4 texture parameters such as energy, entropy, moment of inertia, correlation and the like are calculated, wherein the calculation formula of the energy ASM is as follows:
in the formula, the average value of the gray scale,in order to smooth the average value of the average,as a standard deviation of the gray scale,in order to smooth out the standard deviation of the standard deviation,、、、the calculation methods of (A) are respectively as follows:
connecting the mean values and standard deviations of the energy, entropy, moment of inertia and correlation 4 texture parameters on four angles in series to serve as a template characteristic matrix of the image;
4.5) comparing the characteristics, namely subtracting the characteristic matrix to be matched from the characteristic matrix of the template to obtain a difference matrix A, and then calculating the Euclidean norm of the matrix A, wherein the norm of the matrix A can be realized by solving the maximum singular value of the matrix, and the calculation formula is as follows:
in the formula,representing singular values of the difference matrix A, and T represents a maximum singular value;
4.6) selecting a threshold value by utilizing the size of the maximum singular value T, and carrying out image identification according to the threshold value.
Example 2
Referring to fig. 2, the present invention relates to a vein recognition apparatus based on a gray-scale co-occurrence matrix, which includes:
1) a normalization preprocessing module, configured to perform scale normalization preprocessing on the vein image, where the normalization preprocessing module is configured to implement the function of step 1) in embodiment 1.
2) And the filtering processing module is used for performing filtering processing on the vein image subjected to the normalization preprocessing by using homomorphic filtering, and the filtering processing module is used for realizing the function of the step 2) in the embodiment 1.
3) And the enhancement processing module is used for performing enhancement processing on the filtered vein image by using a local contrast enhancement method, and is used for realizing the function of the step 3) in the embodiment 1.
4) An image recognition module, configured to recognize the enhanced vein image based on the gray level co-occurrence matrix, where the image recognition module is configured to implement the function in step 4) in embodiment 1.
Obviously, the vein recognition apparatus based on the gray-scale co-occurrence matrix of the present embodiment can implement the vein recognition method described in embodiment 1.
The present invention has been described in detail with reference to the embodiments, but the description is only for the preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (6)
1. A vein identification method based on a gray level co-occurrence matrix is characterized in that: which comprises the following steps:
1) carrying out scale normalization pretreatment on the vein image;
2) filtering the vein image after the normalization pretreatment by using homomorphic filtering;
3) enhancing the filtered vein image by using a local contrast enhancement method;
4) the vein image after enhancement processing is identified based on the gray level co-occurrence matrix, and the method specifically comprises the following steps:
4.1) carrying out feature compression on the image;
4.2) solving the gray level co-occurrence matrixGray level co-occurrence matrixIs shown asIn the direction and at a distanceHas a gray value respectivelyAndprobability of occurrence, then pixel pairAndgray level co-occurrence matrix in four directionsThe calculation formula of (2) is as follows:
in the formula,is a pointIs determined by the gray-scale value of (a),is a pointIs determined by the gray-scale value of (a),Lis a gray scale level of the image,andrepresenting the number of lines and rows of the vein image;
4.3) co-occurrence matrix according to gray levelTaking a distanceIs an angle of 10 degree, 45 degrees, 90 degrees and 135 degrees respectively, and then the gray level co-occurrence matrix is alignedAnd (4) carrying out normalization, wherein the calculation formula is as follows:
in the formula,in order to normalize the co-occurrence matrix,for the normalized constant, the calculation formula is:
in the formula,Mthe size of the symbiotic matrix is 8, 16 and 32;
4.4) obtaining each texture characteristic,i.e. to gray level co-occurrence matrix4 texture parameters of energy, entropy, moment of inertia and correlation are calculated, wherein the calculation formula of the energy ASM is as follows:
in the formula,is the mean value of the gray levels,in order to smooth the average value of the average,as a standard deviation of the gray scale,in order to smooth out the standard deviation of the standard deviation,、、、the calculation methods of (A) are respectively as follows:
connecting the mean values and standard deviations of the energy, entropy, moment of inertia and correlation 4 texture parameters on four angles in series to serve as a template characteristic matrix of the image;
4.5) comparing the characteristics, namely subtracting the characteristic matrix to be matched from the characteristic matrix of the template to obtain a difference matrix A, and then calculating the Euclidean norm of the matrix A, wherein the norm of the matrix A can be realized by solving the maximum singular value of the matrix, and the calculation formula is as follows:
in the formula,representing the singular values of the difference matrix A, T representing the maximumA large singular value;
4.6) selecting a threshold value by utilizing the size of the maximum singular value T, and carrying out image identification according to the threshold value.
2. The vein identification method based on the gray level co-occurrence matrix according to claim 1, wherein: in the step 1), a bilinear interpolation method is adopted to carry out scale normalization processing on the vein image.
3. The vein identification method based on the gray level co-occurrence matrix according to claim 1, wherein: the specific way of performing filtering processing on the vein image after the normalization preprocessing by using homomorphic filtering in the step 2) is as follows:
in the formula,is a vein image after the logarithmic transformation,xandyrespectively a row coordinate and a column coordinate of the vein image matrix;
2.2) taking Fourier transform on two sides of the above formula, and enabling:
in the formula,for the logarithmically transformed vein imageThe fourier transformed image of (a) is,uandvrespectively x and y are variables obtained by Fourier transform,is Fourier transform;
2.3) passing Gaussian filterFiltering the above formula to obtain a filtered frequency domain imageThe calculation formula is as follows:
in the formula:is a high-frequency amplification factor, has a value range of more than 1 and less than 2,the offset is a parameter with the value range of more than 0 and less than 1 and controlling the slope of the filterIs the center of the filter and is,is the mid-point in the frequency domain,in order to gain the gain at high frequency,in order to gain the gain at low frequencies,cfor the sharpening constant, the value range is;
2.4) carrying out inverse Fourier transform on the filtered vein image to obtain an image with a frequency domain converted into a space domainThat is, the final filtered image is obtained, and the formula of the inverse fourier transform is:
4. The vein identification method based on the gray level co-occurrence matrix according to claim 3, wherein: the step 3) of enhancing the filtered vein image by using the local contrast enhancement method specifically comprises the following steps:
3.1) filtering the processed imagePerforming exponential operation to obtain homomorphic filtered image:
3.2) settingIs an imageThe gray value of a certain point in (b),iandjrespectively being said imagesRow and column coordinates of the matrix toAs a center, the window size is (2)n+1)*(2nThe region of +1) is a local region, whereinnCalculating a local average value for a positive integerAnd local variance,
The local mean is calculated as:
the calculation formula of the local variance is as follows:
in the formula,is the average of the local areas and is,the local standard deviation is taken as the local standard deviation,aandbrespectively are horizontal and vertical coordinates in a local area;
3.3) is provided withIs composed ofAnd corresponding to the enhanced pixel value, wherein the calculation formula of the enhanced pixel value is as follows:
then
5. The vein identification method based on the gray level co-occurrence matrix according to claim 1, wherein: the step 4.1) of compressing the image features means compressing the gray scale of the original image, i.e. quantizing 256 gray scales into 16 gray scales, and converting 0-255 gray scales into 0-15 gray scales.
6. A vein recognition device based on a gray level co-occurrence matrix is characterized in that: it includes:
1) the normalization preprocessing module is used for carrying out scale normalization preprocessing on the vein image;
2) the filtering processing module is used for carrying out filtering processing on the vein image after the normalization preprocessing by using homomorphic filtering;
3) the enhancement processing module is used for enhancing the filtered vein image by using a local contrast enhancement method;
4) an image identification module for identifying the enhanced vein image based on the gray level co-occurrence matrix,
the method comprises the following specific steps:
4.1) carrying out feature compression on the image;
4.2) solving the gray level co-occurrence matrixGray level co-occurrence matrixIs shown asIn the direction and at a distanceHas a gray value respectivelyAndprobability of occurrence, then pixel pairAndgray level co-occurrence matrix in four directionsThe calculation formula of (2) is as follows:
in the formula,is a pointIs determined by the gray-scale value of (a),is a pointIs determined by the gray-scale value of (a),Lis a gray scale level of the image,andrepresenting the number of lines and rows of the vein image;
4.3) co-occurrence matrix according to gray levelTaking a distanceIs an angle of 10 degree, 45 degrees, 90 degrees and 135 degrees respectively, and then the gray level co-occurrence matrix is alignedAnd (4) carrying out normalization, wherein the calculation formula is as follows:
in the formula,in order to normalize the co-occurrence matrix,for the normalized constants, the calculation formula is:
in the formula,Mthe size of the symbiotic matrix is 8, 16 and 32;
4.4) obtaining each texture characteristic, namely, the gray level co-occurrence matrix4 texture parameters of energy, entropy, moment of inertia and correlation are calculated, wherein the calculation formula of the energy ASM is as follows:
in the formula, the average value of the gray scale,in order to smooth the average value of the average,as a standard deviation of the gray scale,in order to smooth out the standard deviation of the standard deviation,、、、the calculation methods of (A) are respectively as follows:
connecting the mean values and standard deviations of 4 texture parameters of energy, entropy, moment of inertia and correlation in series at four angles to serve as a template characteristic matrix of the image;
4.5) comparing the characteristics, namely subtracting the characteristic matrix to be matched from the characteristic matrix of the template to obtain a difference matrix A, and then calculating the Euclidean norm of the matrix A, wherein the norm of the matrix A can be realized by solving the maximum singular value of the matrix, and the calculation formula is as follows:
in the formula,representing the singular value of the difference matrix A, and T represents the maximum singular value;
4.6) selecting a threshold value by utilizing the size of the maximum singular value T, and carrying out image identification according to the threshold value.
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