CN113496246A - Image identification method based on display screen display - Google Patents
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Abstract
The application provides an image identification method based on display screen display, which comprises the following steps: s1, the user stores the image to be identified into a big data original library in advance; s2, capturing the selected image by the image capturing module; and S3, highlighting the four large directions of the definition, the brightness, the angle and the color of the captured image. This application is through the big data primitive storehouse of image, the image capture module, show regional module, image feature extracts the module, image SVM classification module, the flow cooperation of image analysis identification module and image result display module, can mark the pipe fitting sensitive information of image and catch and draw, from image brightness, the colour, the mark discernment is carried out to the multiple direction of angle and definition, improve the whole discernment precision of image, reduce the misrecognition rate of image, also carry out the split-screen display through a plurality of displays by the picture split-screen module simultaneously, the display viewing effect of reinforcing image information.
Description
Technical Field
The application relates to the field of image recognition, in particular to an image recognition method based on display screen display.
Background
Image recognition, which is a technology for processing, analyzing and understanding images by using a computer to recognize targets and objects in various modes, is a practical application of a deep learning algorithm, and is generally divided into face recognition and commodity recognition at the present stage, wherein the face recognition is mainly applied to security inspection, identity verification and mobile payment; the commodity identification is mainly applied to the commodity circulation process, in particular to the field of unmanned retail such as unmanned goods shelves and intelligent retail cabinets.
Along with the development of the existing internet, the image information on the internet is irregular, the pornographic images, violent images and other junk images are easy to appear to harm minors, so that an image identification method is needed to identify the images, however, the existing image identification method mostly uses skin color as important information to carry out image identification detection, the junk images are easy to cause the conditions of low identification efficiency and high false recognition rate in the identification process, meanwhile, most image display results are displayed by a display, the junk images cannot be displayed in a screen-divided mode, and the display effect of the junk images is greatly reduced.
Therefore, it is necessary to provide an image recognition method based on display screen display to solve the above technical problems.
Disclosure of Invention
The application provides an image identification method based on display screen display, and solves the problems that the existing image identification method is low in identification efficiency, high in false identification rate and low in display effect of garbage images.
In order to solve the technical problem, the image identification method based on display screen display provided by the application comprises the following steps:
s1, the user stores the image to be identified into a big data original library in advance;
s2, capturing the selected image by the image capturing module;
s3, marking the four large directions of the definition, the brightness, the angle and the color of the captured image obviously;
s4, extracting the image features of the marked image, and extracting the key of the color and texture of the marked image;
s5, after the key images are extracted, carrying out partition classification processing through an SVM classification module, and carrying out partition classification on different marked image information;
s6, matching and analyzing the marked images in different partitions based on the cloud computing system platform;
and S7, obtaining the display picture of the image information after matching analysis by the picture obtaining module, and receiving a screen splitting instruction to perform screen splitting processing. (ii) a
And S8, respectively generating pictures of the split image information, respectively processing the generated pictures, intelligently adjusting the frame number, the definition and the color saturation of the pictures, finally, performing screen projection processing on the intelligently adjusted pictures through the picture screen projection module, sending the pictures to each display corresponding to the picture split screen module, and performing split screen display on the pictures through the corresponding monitors.
Optionally, in S1, the single image in the original large image data library is divided into 16 mesh areas according to a4 × 4 division manner, and 4 sub-areas are further divided in a single mesh area.
Optionally, the code numbers of the 16 grid regions are A, B, C, D, E, F, G, H, I, J, K, L, M, N, O and P, respectively, and 4 sub-regions are a1, a2, A3 and a4, B1, B2, B3 and B4, C1, C2, C3 and C4, D1, D2, D3 and D4, E1, E2, E3 and E4, F1, F2, F3 and F4, G1, G2, G3 and G4, H1, H2, H3 and H4, I1, I2 and I2, J2 and J2, K2 and K2, L2 and L2, M2, N72, and N2.
Optionally, in S4, the method for extracting the color feature of the label image includes: respectively solving a first-order distance, a second-order distance and a third-order distance of the image of the salient region in the I, II and III 3 color spaces, namely calculating the average value, the standard deviation and the skewness formula of the image of the salient region on the three color components of I, II and III as follows:
in the formula: f (x, y) -each color component image in iiiii space, i ═ { i, ii, iii }, N-the total number of pixels of the image or sub-region image, then a total of 9 feature parameters are obtained by color feature extraction, as shown below
Fc＝(Ck 1，Ck 2，Ck 3，Ck 4，Ck 5，Ck 6，Ck 7，Ck 8，Ck 9，)。
Optionally, in S4, the method adopted for extracting the texture features of the labeled image is a Gabor filtering method, which is a two-dimensional Gabor filtering function provided by Daugman for simulating the spatial superposition property of the perception fields of simple cells in the cerebral visual cortex, and the two-dimensional Gabor filtering function formula is as follows:
x′＝x cosθ+v sinθ
y′＝-x sinθ+y cosθ
wherein: theta ∈ [0, x) — the direction of the filter, phi ∈ (-pi, pi ] — the phase of the gaussian function, sigma-the variance of the gaussian function, determines the bandwidth of the filter, lambda-the wavelength, 1/lambda-the frequency of cos (2 pi x'/lambda + psi), gamma-determines the aspect ratio of the gaussian convolution kernel g-space.
Optionally, in S8, the number of the displays and the monitors of the screen splitting module is the same, and a one-to-one responsible manner is adopted to perform the screen splitting control, and the image display manner of the display is PPT.
Compared with the related art, the image identification method based on display screen display has the following beneficial effects:
the application provides an image identification method based on display screen display,
1. the method has the advantages that through the matching of the flows of the image big data original library, the image capturing module, the salient region module, the image feature extracting module, the image SVM classification module, the image analyzing and identifying module and the image result displaying module, the mark capturing and extracting of the pipe fitting sensitive information of the image can be carried out, the mark identification is carried out from multiple directions of the image brightness, color, angle and definition, the integral identification accuracy of the image is improved, the image misrecognition rate is reduced, meanwhile, the image split-screen module carries out split-screen display through multiple displays, and the display viewing effect of the image information is enhanced;
2. this application just divides into 16 net regions according to 4 x 4 partition modes through a single picture image altogether, carry out regional division with the geometric form single picture image, it captures the extraction to be convenient for image feature extraction module to refine the characteristic to the key sensitive information in the image, improve the whole discernment precision and the high efficiency of image, avoid the key sensitive information in the image to appear discerning and omitting the phenomenon, code number through 16 net regions is A, B, C, D, E, F, G, H, I, J, K, L, M, N, O and P respectively, can carry out code numbering to net region and subregion, avoid the region confusion mistake to appear, further improve image identification's precision, the method of extracting the adoption through mark image color feature does: the method comprises the steps of respectively calculating the first-order distance, the second-order distance and the third-order distance of a salient region image in I, II and III color spaces, utilizing a color distance mode to carry out feature display on image colors, respectively carrying out accurate formula calculation on the average value, the standard deviation and the deviation of the I, II and III colors, further enhancing the identification accuracy of the image colors, adopting a Gabor filtering method as a method for marking image texture feature extraction, being capable of reducing uncertainty of space and frequency to the maximum extent, simultaneously being capable of detecting edges and lines in different directions and angles in the image, using a PPT as an image display mode through a display, being convenient for a user to read and view, quickly knowing the meaning of an image identification result, being convenient for the user to quickly judge the identified image result, and simultaneously being convenient for the user to take corresponding measures.
Drawings
FIG. 1 is a flowchart of a method of a preferred embodiment of the present application for a method of image recognition based on display screen display;
FIG. 2 is a system flow diagram of the method for image recognition based on a display screen display shown in FIG. 1;
FIG. 3 is a diagram of image region division of the image big data raw library shown in FIG. 2;
FIG. 4 is a system block diagram of the salient region module shown in FIG. 2;
fig. 5 is a system block diagram of the image result display module shown in fig. 2.
Detailed Description
The present application will be further described with reference to the accompanying drawings and embodiments.
Please refer to fig. 1, fig. 2, fig. 3, fig. 4 and fig. 5 in combination, wherein fig. 1 is a flowchart illustrating a method of an image recognition method based on display screen display according to a preferred embodiment of the present application; FIG. 2 is a system flow diagram of the method for image recognition based on a display screen display shown in FIG. 1; FIG. 3 is a diagram of image region division of the image big data raw library shown in FIG. 2; FIG. 4 is a system block diagram of the salient region module shown in FIG. 2; fig. 5 is a system block diagram of the image result display module shown in fig. 2. The image identification method based on display screen display comprises the following steps:
s1, the user stores the image to be identified into a big data original library in advance;
s2, capturing the selected image by the image capturing module;
s3, marking the four large directions of the definition, the brightness, the angle and the color of the captured image obviously;
s4, extracting the image features of the marked image, and extracting the key of the color and texture of the marked image;
s5, after the key images are extracted, carrying out partition classification processing through an SVM classification module, and carrying out partition classification on different marked image information;
s6, matching and analyzing the marked images in different partitions based on the cloud computing system platform;
and S7, obtaining the display picture of the image information after matching analysis by the picture obtaining module, and receiving a screen splitting instruction to perform screen splitting processing. (ii) a
And S8, respectively generating pictures of the split image information, respectively processing the generated pictures, intelligently adjusting the frame number, the definition and the color saturation of the pictures, finally, performing screen projection processing on the intelligently adjusted pictures through the picture screen projection module, sending the pictures to each display corresponding to the picture split screen module, and performing split screen display on the pictures through the corresponding monitors.
In S1, a single image in the image big data original library is divided into 16 grid regions in total according to a4 × 4 division manner, 4 sub-regions are further divided in the single grid region, and the single image is divided into regions in a geometric form, so that the image feature extraction module can conveniently perform refined feature capture and extraction on the key sensitive information in the image, the overall recognition accuracy and efficiency of the image are improved, and the recognition omission phenomenon of the key sensitive information in the image is avoided.
The code numbers of the 16 grid areas are P, the 4 sub-areas are A, A and A, B and B, C and C, D and D, E and E, F and F, G and G, H and H, I and I, J and J, K and K, L and L, M and M, N and N, O and P, P and P, and can code the grid areas and the sub-areas, thereby avoiding the areas from being confused and wrong and further improving the accuracy of image identification.
In S4, the method for extracting the color feature of the label image is as follows: respectively solving a first-order distance, a second-order distance and a third-order distance of the image of the salient region in the I, II and III 3 color spaces, namely calculating the average value, the standard deviation and the skewness formula of the image of the salient region on the three color components of I, II and III as follows:
in the formula: f (x, y) -each color component image in iiiii space, i ═ { i, ii, iii }, N-the total number of pixels of the image or sub-region image, then a total of 9 feature parameters are obtained by color feature extraction, as shown below
Fc＝(Ck 1，Ck 2，Ck 3，Ck 4，Ck 5，Ck 6，Ck 7，Ck 8，Ck 9And displaying the characteristics of the image colors by using a color distance mode, and respectively carrying out accurate formula calculation on the average value, the standard deviation and the deviation of the three colors I, II and III so as to further enhance the identification accuracy of the image colors.
In S4, the method adopted for extracting the texture features of the labeled image is a Gabor filter method, which is a two-dimensional Gabor filter function provided by Daugman for simulating the spatial superposition property of the perception fields of simple cells in the visual cortex of the brain, and the two-dimensional Gabor filter function formula is as follows:
x′＝x cosθ+y sinθ
y′＝-x sinθ+y cosθ
wherein: theta belongs to [0, x) — the direction of the filter, phi belongs to (-pi, pi ] — the phase of the gaussian function, sigma-the variance of the gaussian function, the bandwidth of the filter is determined, lambda-the wavelength, 1/lambda-the frequency of cos (2 pi x'/lambda + psi), and gamma-the aspect ratio of the gaussian convolution kernel function g space is determined, the uncertainty of the space and the frequency can be reduced to the maximum degree, and simultaneously edges and lines in different directions and angles in the image can be detected.
In S8, the number of the displays and the monitors of the screen splitting module is the same, and a one-to-one responsible mode is adopted for screen splitting control, and the image display mode of the display is PPT, which is convenient for the user to read and view, and quickly understand the meaning of the image recognition result, so that the user can quickly determine the recognized image result, and simultaneously, the user can take corresponding measures.
Compared with the related art, the image identification method based on display screen display has the following beneficial effects:
this application is through the big data primitive storehouse of image, the image capture module, show regional module, image feature extracts the module, image SVM classification module, the flow cooperation of image analysis identification module and image result display module, can mark the pipe fitting sensitive information of image and catch and draw, from image brightness, the colour, the mark discernment is carried out to the multiple direction of angle and definition, improve the whole discernment precision of image, reduce the misrecognition rate of image, also carry out the split-screen display through a plurality of displays by the picture split-screen module simultaneously, the display viewing effect of reinforcing image information.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are intended to be included within the scope of the present application.
Claims (10)
1. An image identification method based on display screen display is characterized by comprising the following steps:
s1, the user stores the image to be identified into a big data original library in advance;
s2, capturing the selected image by the image capturing module;
s3, marking the four large directions of the definition, the brightness, the angle and the color of the captured image obviously;
s4, extracting the image features of the marked image, and extracting the key of the color and texture of the marked image;
s5, after the key images are extracted, carrying out partition classification processing through an SVM classification module, and carrying out partition classification on different marked image information;
s6, matching and analyzing the marked images in different partitions based on the cloud computing system platform;
and S7, obtaining the display picture of the image information after matching analysis by the picture obtaining module, and receiving a screen splitting instruction to perform screen splitting processing.
2. The method for image recognition based on display screen display of claim 1, wherein the single image in the original library of large image data at S1 is divided into 16 grid areas according to a 4-by-4 division manner, and 4 sub-areas are further divided into a single grid area.
3. The method for image recognition based on display screen display according to claim 1, wherein in S4, the method for extracting color features of the label image is as follows: and respectively obtaining the first step distance, the second step distance and the third step distance of the image of the salient region in the I, II and III 3 color spaces.
4. The method of claim 3, wherein the average, standard deviation and skewness formula of the three color components of I, II and III are as follows:
in the formula: f (x, y) -each color component image in iiiii space, i ═ { i, ii, iii }, N-the total number of pixels of the image or sub-region image, then a total of 9 feature parameters are obtained by color feature extraction, as shown below
Fc＝(Ck 1，Ck 2，Ck 3，Ck 4，Ck 5，Ck 6，Ck 7，Ck 8，Ck 9，)。
5. The method for image recognition based on display screen display of claim 1, wherein in S4, the method for extracting texture features of the labeled image is a Gabor filter method, which is a two-dimensional Gabor filter function proposed by Daugman for simulating spatial overlay properties of the perceptual visual field of simple cells in the cerebral visual cortex, and the two-dimensional Gabor filter function formula is as follows:
x′＝x cosθ+y sinθ
y′＝-x sinθ+y cosθ
wherein: theta ∈ [0, x) — the direction of the filter, phi ∈ (-pi, pi ] — the phase of the gaussian function, sigma-the variance of the gaussian function, determines the bandwidth of the filter, lambda-the wavelength, 1/lambda-the frequency of cos (2 pi x'/lambda + psi), gamma-determines the aspect ratio of the gaussian convolution kernel g-space.
6. The image recognition method based on display screen display of claim 1, further comprising:
and S8, generating the images of the split images, and processing the images.
7. The image recognition method based on display screen display of claim 6, further comprising:
the picture processing includes: the frame number, definition and color saturation of the picture are intelligently adjusted.
8. The display-screen-display-based image recognition method of claim 7, wherein S8 further comprises: and the intelligently adjusted pictures are subjected to screen projection processing by the picture screen projection module and then are sent to each display corresponding to the picture screen splitting module, and the pictures are subjected to screen splitting display by the corresponding monitor.
9. The method for image recognition based on OSD of claim 8, wherein in step S8, the number of the monitors and the display devices of the OSD are the same, and the OSD is controlled in a one-to-one manner.
10. The method for image recognition based on display screen display of claim 9, further comprising:
the image display mode of the display is PPT.
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| CN114842578A (en) * | 2022-04-26 | 2022-08-02 | 深圳市凯迪仕智能科技有限公司 | Intelligent lock, shooting control method and related device |
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