CN114354138A - Screen stroboscopic detection system and method based on image processing - Google Patents
Screen stroboscopic detection system and method based on image processing Download PDFInfo
- Publication number
- CN114354138A CN114354138A CN202111660795.5A CN202111660795A CN114354138A CN 114354138 A CN114354138 A CN 114354138A CN 202111660795 A CN202111660795 A CN 202111660795A CN 114354138 A CN114354138 A CN 114354138A
- Authority
- CN
- China
- Prior art keywords
- image
- stroboscopic
- screen
- images
- shutter speed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000001514 detection method Methods 0.000 title claims abstract description 23
- 238000007781 pre-processing Methods 0.000 claims abstract description 24
- 235000011034 Rubus glaucus Nutrition 0.000 claims description 14
- 235000009122 Rubus idaeus Nutrition 0.000 claims description 14
- 240000007651 Rubus glaucus Species 0.000 claims description 13
- 238000001914 filtration Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 7
- 230000003247 decreasing effect Effects 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 208000019695 Migraine disease Diseases 0.000 description 1
- 244000235659 Rubus idaeus Species 0.000 description 1
- 208000003464 asthenopia Diseases 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000005802 health problem Effects 0.000 description 1
- 206010027599 migraine Diseases 0.000 description 1
- 208000001491 myopia Diseases 0.000 description 1
- 230000004379 myopia Effects 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Landscapes
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
The invention provides a screen stroboscopic detection system and method based on image processing, which aim to reduce the process complexity of a traditional screen stroboscopic analyzer and achieve the aims of simplifying the screen stroboscopic detection process and reducing the detection cost. The invention provides a screen stroboscopic detection method based on image processing, which comprises the following specific steps: step 1, aligning a camera to an electronic screen to be detected; initializing the shutter speed to obtain three frames of images; step 2, preprocessing the image collected by the camera; step 3, traversing the preprocessed black-and-white images according to columns; the traversed image only contains black stripes, and the width of the black stripes is calculated and stored; step 4, repeating the step 2 and the step 3, processing the continuously acquired three frames of images, judging whether a stroboscopic feature exists, and if not, calculating a screen stroboscopic value; if so, the shutter speed is reduced and steps 2, 3 and 4 are repeated.
Description
Technical Field
The invention belongs to the technical field of screen stroboscopic detection, and particularly relates to a screen stroboscopic detection system and method based on image processing.
Background
In recent years, with the mass popularization of electronic products such as mobile phones and computers, people have longer and longer time to face electronic screens in study, work and life, however, screen stroboflash is an important light quality factor of the screens, and poor screen stroboflash not only influences people to light quality perception and causes discomfort, but also stimulates visual nerves of people to further cause health problems such as visual fatigue, teenager myopia, migraine and the like. The influence of the screen stroboflash on the human body is paid more and more attention by people, and accordingly, how to detect the screen stroboflash is also paid more and more attention by people. At present, professional instruments for screen stroboscopic detection are fewer and more expensive in the market. The method mainly comprises the steps of 1, collecting screen optical signals 2 through an optical signal sensor, carrying out fast Fourier transform on the optical signals to obtain signal frequency domain information 3, and analyzing and processing the optical signals in a frequency domain to obtain stroboscopic information.
Stroboscopic: the OLED electronic screen is driven by PWM waves, the brightness value of the screen is adjusted, and the stroboscopic phenomenon can be seen under a camera with a high shutter speed.
Shutter speed: shutter speed is an important parameter for digital camera shutters, and generally the faster the shutter speed, the shorter the image exposure time.
Raspberry pie: raspberry Pi (chinese name "Raspberry Pi", abbreviated RPi, (or RasPi/RPi)) is just a credit card sized microcomputer, the system of which is based on Linux.
Disclosure of Invention
Because the electronic screen is visible everywhere, people face the screen for a longer time, the quality of the electronic screen is concerned more and more, but the existing screen stroboscopic analyzer is relatively complex in analysis process and expensive in price, and cannot be afforded by ordinary individuals. Aiming at the defects, the invention provides a screen stroboscopic detection system and method based on image processing, so as to reduce the process complexity of the traditional screen stroboscopic analyzer, and achieve the purposes of simplifying the screen stroboscopic detection process and reducing the detection cost.
The invention provides a screen stroboscopic detection method based on image processing, which comprises the following specific steps:
step 1, aligning a camera to an electronic screen to be detected; initializing the shutter speed to 3000, and continuously acquiring three frames of images;
step 2, carrying out image preprocessing on the image acquired by the camera, wherein the preprocessing comprises median filtering processing, denoising processing and image binarization processing;
step 3, traversing the preprocessed black-and-white image according to rows, wherein if all pixels in a row are not zero, all the pixels in the row are one; the traversed image only contains black stripes, and the width of the black stripes is calculated and maintained;
step 4, repeating the step 2 and the step 3, processing the continuously acquired three frames of images, wherein if the image results are all white images, the images do not contain black stripes, and the shutter speed value is less than or equal to the screen stroboscopic frequency; if the image results all contain black stripes, calculating the average value of the widths of the black stripes of the three images, comparing the width average value with the average value of the widths of the black stripes of the last three images, if the width average value is smaller than the last width average value, reducing the shutter speed, and repeating the steps 1 to 4 until the image does not have the characteristics of the black stripes, wherein the shutter speed is matched with the screen stroboscopic frequency, and the screen stroboscopic frequency is equal to the shutter speed value.
Preferably, the step 2 includes:
step 2a, converting the image into a gray image, and then performing Gaussian denoising processing to remove redundant Gaussian noise of the image;
step 2b, median filtering processing is carried out on the image, and most of image features except the black stripes are filtered out;
and 2c, carrying out binarization on the image, wherein all pixel values of the black stripes become zero at the moment, and most pixel values of the non-black stripes are one.
Preferably, the step 3 includes:
step 3a, traversing each pixel from top to bottom and then from left to right of each binarized image according to columns;
step 3b, judging whether a row of pixel values are all zero, if so, keeping the stroboscopic characteristic, otherwise, skipping to the step 3 c;
and 3c, setting all the pixel values of the row to be one.
Preferably, in step 4, the setting value for decreasing the shutter speed is 200.
The image preprocessing process comprises the steps of carrying out gray processing on the image, then using a Gaussian low-pass filter to carry out denoising, then using a median filtering method to remove other characteristic images, and finally carrying out binarization on the image, so that the processing and calculation are convenient.
The process of pixel traversal of the binary image is that firstly all pixels are traversed from top to bottom and then from left to right, and if the pixel values in a row are all zero, the pixels are kept unchanged; if a row of pixel values is not all zero, then the row of pixel values is set to one.
If the image result shows that the stroboscopic feature is contained, revising and reducing the shutter speed, acquiring the image and judging whether the stroboscopic feature is contained or not until the image does not contain the stroboscopic feature; if the image result does not contain the stroboscopic feature, the shutter speed is matched with the stroboscopic frequency, and the stroboscopic frequency can be calculated.
A system for image processing based screen strobe detection, comprising: a camera and a raspberry pie;
the camera is connected with the raspberry pie through a USB 3.0;
the raspberry pie comprises:
an image acquisition module: the system comprises a camera, a flash detector, a flash controller and a controller, wherein the camera is used for acquiring continuous images of an electronic screen during operation and performing flash detection by using the images;
an image preprocessing module: the image preprocessing module is used for preprocessing the acquired image, wherein the preprocessing comprises graying, denoising, filtering and binaryzation;
an image pixel traversal module: the system is used for traversing all pixels of an image, and only the black-stripe stroboscopic feature is reserved;
stroboscopic characteristic judgment module: the stroboscopic detection device is used for judging whether the image contains a black stripe stroboscopic feature or not, and if so, continuing to circularly detect; if the strobe characteristic is not contained, the shutter speed is matched with the strobe frequency;
a strobe frequency calculation module: the system is used for calculating the screen stroboscopic frequency, and the screen stroboscopic frequency is equal to the reciprocal of the shutter speed value at the moment;
the input of image acquisition module links to each other with the camera, and the output links to each other with image preprocessing module, and image preprocessing module's output links to each other with image pixel traversal module, and image pixel traversal module's output links to each other with stroboscopic characteristic judgment module, and stroboscopic characteristic judgment module's output and stroboscopic frequency calculation module.
1. The invention has the substantive characteristics that: the existing traditional screen stroboscopic tester has a complex analysis process and is troublesome and laborious to analyze; the stroboscopic analysis process is simple, the thought method of image processing is combined with stroboscopic analysis, and the screen stroboscopic can be intuitively analyzed through an image processing algorithm.
2. The conventional screen stroboscopic analyzer is expensive, and the screen stroboscopic analysis can be completed by using a common camera and the raspberry pi 3B, so that the cost is greatly reduced, and the popularization of the public is facilitated.
3. The stability is good, the device can adapt to various environments, and various electronic screens can be detected; and the algorithm principle is simple, the program complexity is low, and the running speed is high.
Drawings
FIG. 1 is a block diagram of the apparatus of the present invention;
FIG. 2 is a general flow diagram of the present invention;
FIG. 3 is a diagram of a method embodying the present invention;
FIG. 4 is a block diagram of the system of the present invention;
FIG. 5 is a flow chart of the present invention for determining whether a strobe feature exists in an image.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention easier to understand by those skilled in the art, and thus will clearly and clearly define the scope of the invention.
Example 1
The apparatus structure is shown in fig. 1: the camera is responsible for obtaining the screen image, and the raspberry group is responsible for carrying out image processing to the image of obtaining, and camera and raspberry group pass through USB3.0 and connect, finally calculate electronic screen's stroboscopic frequency.
The overall flow is shown in fig. 2, and the overall flow is as follows:
connecting a camera with the raspberry pie through a USB3.0, starting the raspberry pie to start the camera, and aligning the camera with an electronic screen to be detected; initializing the shutter speed, wherein the speed is initialized to 3000, and the stroboscopic frequency of a common electronic screen is not more than 3000; three images are continuously acquired.
And carrying out image preprocessing on the image acquired by the camera through a raspberry group, wherein the preprocessing comprises median filtering processing, denoising processing and then image binarization processing.
And traversing the preprocessed black-and-white image according to rows, wherein if all pixels in a row are not zero, all the pixels in the row are one, and the method is used for clearing other images except the stroboscopic characteristic black stripe. The traversed image contains only black stripes (strobe feature) and the width of the black stripes is calculated and saved in preparation for later iteration to calculate the strobe frequency.
And processing the three acquired images according to the method. If the image results are all white images, the images do not contain black stripes, and the shutter speed value is the screen stroboscopic frequency; if the image results all contain black stripes, calculating the average value of the widths of the black stripes of the three images, comparing the width average value with the average value of the widths of the black stripes of the last three images, if the width average value is smaller than the last width average value, properly reducing the shutter speed, and performing image acquisition, processing and calculation again until the image does not have the characteristics of the black stripes, wherein the shutter speed is matched with the screen stroboscopic frequency which is equal to the shutter speed value.
The method is specifically realized as shown in fig. 3: firstly, preprocessing an acquired image, including converting the image into a gray image, and then performing Gaussian denoising processing to remove redundant Gaussian noise of the image.
Carrying out median filtering processing on the three preprocessed images to filter out most of image characteristics except black stripes; and then, carrying out binarization on the image, wherein all pixel values of the black stripes become zero at the moment, and most pixel values of the non-black stripes are one.
This step determines whether the image has stroboscopic features: traversing each pixel from top to bottom and then from left to right according to each row of the binary image, wherein if the pixel values of a row are not all zero, the pixel values of the row are all set to be one if the pixel values of the row do not belong to the black stripe feature; if a row of pixel values is all zero, then it is unchanged. The method is used for eliminating the non-black stripe image features and only keeping the black stripe image features, and the specific algorithm flow is shown in fig. 5.
After the image processing of the previous step, if the image has stroboflash, the image only has black stripe characteristics, and the width of the black stripe is calculated; if there is no strobe, the image pixel values are all one, all white, at which time the screen strobe frequency can be calculated, which is equal to the inverse of the shutter speed value.
In the last step, if the image has stroboflash, the shutter speed is properly reduced (reduced by 200: obtained by multiple experiments), all the steps are repeated until the image has no stroboflash phenomenon, and the shutter speed is matched with the stroboflash frequency, so that the screen stroboflash frequency is calculated.
The detailed process of fig. 4 is as follows:
an image acquisition module: the method is used for acquiring continuous images of the electronic screen during operation by using the camera and using the images to perform strobe detection.
An image preprocessing module: the method is used for preprocessing the acquired image, and the preprocessing comprises graying, denoising, filtering and binarization.
An image pixel traversal module: for traversing all pixels of the image in order to retain only the black-striped strobe feature.
Stroboscopic characteristic judgment module: the stroboscopic detection device is used for judging whether the image contains a black stripe stroboscopic feature or not, and if so, continuing to circularly detect; if the strobe feature is not included, then the shutter speed is said to match the strobe frequency. A strobe frequency calculation module: for calculating the screen strobe frequency, which is equal to the inverse of the shutter speed value at this time.
Claims (5)
1. A screen stroboscopic detection method based on image processing is characterized by comprising the following steps:
step 1, aligning a camera to an electronic screen to be detected; initializing the shutter speed to 3000, and continuously acquiring three frames of images;
step 2, carrying out image preprocessing on the image acquired by the camera, wherein the preprocessing comprises median filtering processing, denoising processing and image binarization processing;
step 3, traversing the preprocessed black-and-white image according to rows, wherein if all pixels in a row are not zero, all the pixels in the row are one; the traversed image only contains black stripes, and the width of the black stripes is calculated and maintained;
step 4, repeating the step 2 and the step 3, processing the continuously acquired three frames of images, wherein if the image results are all white images, the images do not contain black stripes, and the shutter speed value is less than or equal to the screen stroboscopic frequency; if the image results all contain black stripes, calculating the average value of the widths of the black stripes of the three images, comparing the width average value with the average value of the widths of the black stripes of the last three images, if the width average value is smaller than the last width average value, reducing the shutter speed, and repeating the steps 1 to 4 until the image does not have the characteristics of the black stripes, wherein the shutter speed is matched with the screen stroboscopic frequency, and the screen stroboscopic frequency is equal to the shutter speed value.
2. The method for detecting screen stroboscopic based on image processing as claimed in claim 1, wherein the step 2 comprises:
step 2a, converting the image into a gray image, and then performing Gaussian denoising processing to remove redundant Gaussian noise of the image;
step 2b, median filtering processing is carried out on the image, and most of image features except the black stripes are filtered out;
and 2c, carrying out binarization on the image, wherein all pixel values of the black stripes become zero at the moment, and most pixel values of the non-black stripes are one.
3. The method for detecting screen stroboscopic based on image processing as claimed in claim 1, wherein said step 3 comprises:
step 3a, traversing each pixel from top to bottom and then from left to right of each binarized image according to columns;
step 3b, judging whether a row of pixel values are all zero, if so, keeping the stroboscopic characteristic, otherwise, skipping to the step 3 c;
and 3c, setting all the pixel values of the row to be one.
4. The method as claimed in claim 1, wherein in step 4, the shutter speed is decreased by 200.
5. An image processing-based screen stroboscopic detection system is applied to the image processing-based screen stroboscopic detection method of claim 1, and comprises: a camera and a raspberry pie;
the camera is connected with the raspberry pie through a USB 3.0;
the raspberry pie comprises:
an image acquisition module: the system comprises a camera, a flash detector, a flash controller and a controller, wherein the camera is used for acquiring continuous images of an electronic screen during operation and performing flash detection by using the images;
an image preprocessing module: the image preprocessing module is used for preprocessing the acquired image, wherein the preprocessing comprises graying, denoising, filtering and binaryzation;
an image pixel traversal module: the system is used for traversing all pixels of an image, and only the black-stripe stroboscopic feature is reserved;
stroboscopic characteristic judgment module: the stroboscopic detection device is used for judging whether the image contains a black stripe stroboscopic feature or not, and if so, continuing to circularly detect; if the strobe characteristic is not contained, the shutter speed is matched with the strobe frequency;
a strobe frequency calculation module: the system is used for calculating the screen stroboscopic frequency, and the screen stroboscopic frequency is equal to the reciprocal of the shutter speed value at the moment;
the input of image acquisition module links to each other with the camera, and the output links to each other with image preprocessing module, and image preprocessing module's output links to each other with image pixel traversal module, and image pixel traversal module's output links to each other with stroboscopic characteristic judgment module, and stroboscopic characteristic judgment module's output and stroboscopic frequency calculation module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111660795.5A CN114354138A (en) | 2021-12-30 | 2021-12-30 | Screen stroboscopic detection system and method based on image processing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111660795.5A CN114354138A (en) | 2021-12-30 | 2021-12-30 | Screen stroboscopic detection system and method based on image processing |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114354138A true CN114354138A (en) | 2022-04-15 |
Family
ID=81106214
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111660795.5A Pending CN114354138A (en) | 2021-12-30 | 2021-12-30 | Screen stroboscopic detection system and method based on image processing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114354138A (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102523379A (en) * | 2011-11-09 | 2012-06-27 | 哈尔滨工业大学 | Image shooting method under stroboscopic scene and method for processing stroboscopic images obtained by using image shooting method |
| CN104346794A (en) * | 2013-07-25 | 2015-02-11 | 浙江大华技术股份有限公司 | Video flicker detection method and device |
| CN104378628A (en) * | 2014-11-28 | 2015-02-25 | 广东中星电子有限公司 | Method for detecting flickering stripes in images |
| CN107111882A (en) * | 2016-03-22 | 2017-08-29 | 广东虚拟现实科技有限公司 | Striped set lookup method, device and system |
| CN109166111A (en) * | 2018-08-15 | 2019-01-08 | 苏州富鑫林光电科技有限公司 | A kind of electronic ink screen defect inspection method and system |
| CN109274984A (en) * | 2018-10-16 | 2019-01-25 | 深圳开阳电子股份有限公司 | Detect the method, apparatus and image processing equipment of light source scintillation in video sequence |
| CN109784120A (en) * | 2018-12-11 | 2019-05-21 | 田丰 | Optical code generating means, light sensation data interactive method and image collecting device |
| US20190289191A1 (en) * | 2016-12-28 | 2019-09-19 | Olympus Corporation | Imaging device and flicker detection method |
| CN111246051A (en) * | 2018-11-09 | 2020-06-05 | 浙江宇视科技有限公司 | Method, device, equipment and storage medium for automatically detecting stripes and inhibiting stripes |
| CN111510708A (en) * | 2020-04-24 | 2020-08-07 | 展讯通信（上海）有限公司 | Image stroboscopic detection method and device, storage medium and terminal |
| CN113596344A (en) * | 2021-07-26 | 2021-11-02 | 艾酷软件技术(上海)有限公司 | Shooting processing method and device, electronic equipment and readable storage medium |
| CN113840090A (en) * | 2021-09-27 | 2021-12-24 | 维沃移动通信有限公司 | Shutter adjusting method, device, equipment and storage medium |
-
2021
- 2021-12-30 CN CN202111660795.5A patent/CN114354138A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102523379A (en) * | 2011-11-09 | 2012-06-27 | 哈尔滨工业大学 | Image shooting method under stroboscopic scene and method for processing stroboscopic images obtained by using image shooting method |
| CN104346794A (en) * | 2013-07-25 | 2015-02-11 | 浙江大华技术股份有限公司 | Video flicker detection method and device |
| CN104378628A (en) * | 2014-11-28 | 2015-02-25 | 广东中星电子有限公司 | Method for detecting flickering stripes in images |
| CN107111882A (en) * | 2016-03-22 | 2017-08-29 | 广东虚拟现实科技有限公司 | Striped set lookup method, device and system |
| US20190289191A1 (en) * | 2016-12-28 | 2019-09-19 | Olympus Corporation | Imaging device and flicker detection method |
| CN109166111A (en) * | 2018-08-15 | 2019-01-08 | 苏州富鑫林光电科技有限公司 | A kind of electronic ink screen defect inspection method and system |
| CN109274984A (en) * | 2018-10-16 | 2019-01-25 | 深圳开阳电子股份有限公司 | Detect the method, apparatus and image processing equipment of light source scintillation in video sequence |
| CN111246051A (en) * | 2018-11-09 | 2020-06-05 | 浙江宇视科技有限公司 | Method, device, equipment and storage medium for automatically detecting stripes and inhibiting stripes |
| CN109784120A (en) * | 2018-12-11 | 2019-05-21 | 田丰 | Optical code generating means, light sensation data interactive method and image collecting device |
| CN111510708A (en) * | 2020-04-24 | 2020-08-07 | 展讯通信（上海）有限公司 | Image stroboscopic detection method and device, storage medium and terminal |
| CN113596344A (en) * | 2021-07-26 | 2021-11-02 | 艾酷软件技术(上海)有限公司 | Shooting processing method and device, electronic equipment and readable storage medium |
| CN113840090A (en) * | 2021-09-27 | 2021-12-24 | 维沃移动通信有限公司 | Shutter adjusting method, device, equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 真崎颂也（日），费洛初译: "《摄影丛刊 第15辑》", 31 December 1980, 上海人民美术出版社编辑, pages: 92 - 93 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gu et al. | Learning a no-reference quality assessment model of enhanced images with big data | |
| CN110287791B (en) | Screening method and system for face pictures | |
| CN110473189B (en) | Text image definition judging method and system | |
| CN107911625A (en) | Light measuring method, device, readable storage medium storing program for executing and computer equipment | |
| CN107123124B (en) | Retina image analysis method and device and computing equipment | |
| CN108765406A (en) | A kind of snow mountain detection method based on infrared remote sensing image | |
| CN105678245A (en) | Target position identification method based on Haar features | |
| CN109886931A (en) | Gear ring of wheel speed sensor detection method of surface flaw based on BP neural network | |
| CN116664457B (en) | Image processing method for enhancing denoising | |
| CN112419261A (en) | Visual acquisition method and device with abnormal point removing function | |
| CN109801231A (en) | Image processing method of electrophoresis electronic paper detection equipment | |
| CN100410964C (en) | Acquisition and splicing method of three-face rolling fingerprint | |
| KR20080079798A (en) | Method of face detection and recognition | |
| CN106940904A (en) | Attendance checking system based on recognition of face and speech recognition | |
| CN109815784A (en) | A kind of intelligent method for classifying based on thermal infrared imager, system and storage medium | |
| CN114354138A (en) | Screen stroboscopic detection system and method based on image processing | |
| CN111669575B (en) | Method, system, electronic device, medium and terminal for testing image processing effect | |
| CN116188510A (en) | Enterprise emission data acquisition system based on multiple sensors | |
| CN109793509A (en) | A kind of nuclear radiation detection and method for measuring heart rate and device | |
| WO2022126923A1 (en) | Asc-us diagnosis result identification method and apparatus, computer device, and storage medium | |
| CN115423861A (en) | Gas leakage detection method and device, equipment and storage medium | |
| CN115022617A (en) | Video quality evaluation method based on electroencephalogram signal and space-time multi-scale combined network | |
| Sun et al. | No-contact heart rate monitoring based on channel attention convolution model | |
| CN114004777A (en) | Method and device for detecting broken line of flexible screen, computer equipment and medium | |
| CN111325118A (en) | Method for identity authentication based on video and video equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |