CN110545192B - Network cutting control device - Google Patents
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- CN110545192B CN110545192B CN201910071001.8A CN201910071001A CN110545192B CN 110545192 B CN110545192 B CN 110545192B CN 201910071001 A CN201910071001 A CN 201910071001A CN 110545192 B CN110545192 B CN 110545192B
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Classifications
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- G06T5/70—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/02—Details
- H04L12/12—Arrangements for remote connection or disconnection of substations or of equipment thereof
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- G—PHYSICS
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
- G06T2207/20032—Median filtering
Abstract
The invention relates to a network cutting-off management and control device. The network cutting-off control device is reasonable in design and convenient to apply. Due to the fact that the customized dinner plate of the fast food restaurant exists around each human body in the fast food restaurant on the basis of the image imaging characteristics, whether the human body is a non-dining person or not is determined, and important attention is paid so as to take follow-up measures, such as cutting off a wireless network, and therefore operation efficiency and economic benefits of the fast food restaurant are guaranteed.
Description
Technical Field
The invention relates to the field of network control, in particular to a network cut-off management and control device.
Background
The communication is mainly used for researching the generation of signals, the transmission, the exchange and the processing of information, and the theoretical and engineering application problems in the aspects of computer communication, digital communication, satellite communication, optical fiber communication, cellular communication, personal communication, stratospheric communication, multimedia technology, information expressway, digital program control exchange and the like. With the advent of telegraph creation by the americans, modern communication technologies have been developed. The field of communications has developed rapidly with the ever-increasing level of modern technology in order to accommodate the ever-evolving technological needs.
Disclosure of Invention
The invention aims to provide a network cut-off management and control device, which comprises: the distribution detection equipment is arranged on a side wall of the fast food restaurant, is connected with the feature recognition equipment and is used for carrying out red highlighting marking on a human body area in the noise elimination image so as to show the key attention of non-dining personnel when no dinner plate area exists within a preset distance around the human body area in the noise elimination image; the network control equipment is arranged on the side wall of the fast food restaurant, is connected with the distribution detection equipment and is used for cutting off the wireless routing equipment of the fast food restaurant when the number of human body areas without any dinner plate area in the preset distance around the noise elimination image exceeds the limit; the liquid crystal display device is arranged on the side wall of the fast food restaurant, is connected with the distribution detection device and is used for receiving and displaying the noise elimination image subjected to the red highlighting marking; the on-site camera device is arranged on the wall top of the fast food restaurant and is used for carrying out on-site camera shooting operation on the environment in the fast food restaurant to obtain a corresponding environment image in the restaurant; the first analysis equipment is positioned near the field camera device, is connected with the field camera device, and is used for measuring the contrast of the environment image in the store and outputting the measurement result as real-time contrast; the second analysis equipment is respectively connected with the on-site camera device and the first analysis equipment, and is used for receiving the real-time contrast and the in-store environment image, and performing uniform blocking processing on the in-store environment image based on the real-time contrast to obtain a plurality of corresponding sub-images; a first processing device connected to the second analysis device, configured to receive the plurality of sub-images, and perform the following processing for each sub-image: acquiring R channel values of all pixel points of the subimages, adding the R channel values of all pixel points of the subimages, and outputting the added result as the color parameter of the subimages; and the second processing equipment is connected with the first processing equipment and used for receiving each color parameter of each sub-image, sending out the first control signal when all the color parameters fall within the preset parameter threshold range, and sending out the second control signal when the color parameters fall out of the preset parameter threshold range.
The invention needs to have the following five important invention points:
(1) analyzing whether a fast food restaurant customized dinner plate exists around each human body in a fast food restaurant based on image imaging characteristics so as to determine whether the human body is a non-dining person, and paying attention to the human body so as to take follow-up measures and improve dining efficiency and the number of dining persons of the fast food restaurant;
(2) introducing network control equipment, connecting with the distribution detection equipment, and cutting off wireless routing equipment of the fast food restaurant when the number of human body areas without any dinner plate area in the preset distance around the noise elimination image exceeds the limit, so as to prompt non-dining personnel to leave quickly;
(3) when the bipolar pulse noise amplitude of the processed image is larger than the preset bipolar pulse noise amplitude, executing circulating median filtering processing on the image until the bipolar pulse noise amplitude of the obtained processed image does not exceed the preset bipolar pulse noise amplitude;
(4) in the image signal processing, a corresponding processing strategy is determined based on the uniformity degree of pulse noise distribution in an image, and the processing strategy is corrected based on the target distribution condition in the image, so that the self-adaptive capacity of the image processing is improved;
(5) the segmentation processing of the image output by the on-site camera device is realized based on the real-time contrast of the image, and whether the image output by the on-site camera device meets the focusing standard or not is determined based on the characteristic analysis of the segmented sub-image, so that the on-site camera device can conveniently focus in time.
The network cutting-off control device is reasonable in design and convenient to apply. Due to the fact that the customized dinner plate of the fast food restaurant exists around each human body in the fast food restaurant on the basis of the image imaging characteristics, whether the human body is a non-dining person or not is determined, and important attention is paid so as to take follow-up measures, such as cutting off a wireless network, and therefore operation efficiency and economic benefits of the fast food restaurant are guaranteed.
Detailed Description
A fast food restaurant, or fast food restaurant, is a restaurant that provides food quickly after ordering and maintains service at a minimum. It is not necessary for guests to eat the food very quickly. Often the food provided by these restaurants is referred to as fast food or snack food. Although fast food restaurants are often viewed as a metaphor of modern technological culture, the history of fast food restaurants is likely to be as old as cities, with different appearances between each culture. There are vendors selling bread and olives in ancient roman cities, a shop in east asian culture, and a shop for selling fried bean balls in the middle east. In the uk, traditional take-away foods such as french fries and the like are still ubiquitous in the uk, despite the popularity of chain instant restaurants. At the end of the 20 th century, Italian, Chinese, Indian and other cuisines were added to the foods taken outside.
In the prior art, for a fast food restaurant, the fast movement of diners is desirable to ensure the operational benefits of the fast food restaurant to the maximum, and the actions of occupying dining seats for non-diners and occupying dining seats after diners are taken away are all actions which cause damage to the economic benefits of the fast food restaurant, although the staff of the fast food restaurant can also urge the non-diners to leave in a fast disc-collecting and broadcasting way, however, the actions are often prohibited in order to use the wireless network of the fast food restaurant.
In order to overcome the above disadvantages, the present invention provides a network cut-off management and control device to manage the cut-off of a network, which effectively solves the corresponding technical problems.
The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The network cuts off management and control device includes:
the distribution detection equipment is arranged on a side wall of the fast food restaurant, is connected with the feature recognition equipment and is used for carrying out red highlighting marking on a human body area in the noise elimination image so as to show the key attention of non-dining personnel when no dinner plate area exists within a preset distance around the human body area in the noise elimination image;
the network control equipment is arranged on the side wall of the fast food restaurant, is connected with the distribution detection equipment and is used for cutting off the wireless routing equipment of the fast food restaurant when the number of human body areas without any dinner plate area in the preset distance around the noise elimination image exceeds the limit;
the liquid crystal display device is arranged on the side wall of the fast food restaurant, is connected with the distribution detection device and is used for receiving and displaying the noise elimination image subjected to the red highlighting marking;
the on-site camera device is arranged on the wall top of the fast food restaurant and is used for carrying out on-site camera shooting operation on the environment in the fast food restaurant to obtain a corresponding environment image in the restaurant;
the first analysis equipment is positioned near the field camera device, is connected with the field camera device, and is used for measuring the contrast of the environment image in the store and outputting the measurement result as real-time contrast;
the second analysis equipment is respectively connected with the on-site camera device and the first analysis equipment, and is used for receiving the real-time contrast and the in-store environment image, and performing uniform blocking processing on the in-store environment image based on the real-time contrast to obtain a plurality of corresponding sub-images;
a first processing device connected to the second analysis device, configured to receive the plurality of sub-images, and perform the following processing for each sub-image: acquiring R channel values of all pixel points of the subimages, adding the R channel values of all pixel points of the subimages, and outputting the added result as the color parameter of the subimages;
the second processing device is connected with the first processing device and used for receiving each color parameter of each sub-image, sending out a first control signal when all the color parameters fall within a preset parameter threshold range, and sending out a second control signal when the color parameters fall out of the preset parameter threshold range;
the data output equipment is respectively connected with the second processing equipment and the field camera device, and is used for sending focusing prompt information to the field camera device when receiving the first control signal and not sending any information to the field camera device when receiving the second control signal;
the signal analysis equipment is connected with the field camera device and used for receiving the in-store environment image, extracting characteristic quantity related to sharpening processing of the in-store environment image, inputting the extracted characteristic quantity into a data analysis model consisting of an input layer, an output layer and a plurality of hidden layers, and performing data analysis on the characteristic quantity input by the input layer by layer, wherein the output layer is connected with the last hidden layer and used for outputting the result of data analysis of the last hidden layer, and the output quantity type of the output layer is a sharpening processing type;
the adaptive sharpening device is connected with the signal analysis device and is used for receiving the sharpening processing type and carrying out sharpening operation based on the sharpening processing type on the in-store environment image so as to obtain and output a sharpened image;
the uniformity degree judging device is connected with the self-adaptive sharpening device and used for receiving the sharpened image, detecting a plurality of positions of a plurality of impulse noises in the sharpened image from the sharpened image, and determining the uniformity degree of the distribution of the impulse noises in the sharpened image based on the plurality of positions of the plurality of impulse noises in the sharpened image;
the noise distribution judging device is connected with the uniformity degree judging device and used for receiving the uniformity degree and determining the corresponding removal quantity based on the uniformity degree, wherein the larger the uniformity degree is, the more uniform the pulse noise distribution in the sharpened image is, and the smaller the corresponding removal quantity determined based on the uniformity degree is;
the subimage segmentation device is connected with the uniformity degree judgment device and used for receiving the sharpened image, extracting a plurality of target subimages in which a plurality of targets are respectively positioned from the sharpened image, determining the number of pulse noises in each target subimage based on a plurality of positions of a plurality of pulse noises in the sharpened image, and outputting the target subimage with the largest number of pulse noises as the subimage to be analyzed;
the data matching device is connected with the subimage segmentation device and used for receiving the subimage to be analyzed and determining a search window which is most matched with the subimage to be analyzed based on the shape of the subimage to be analyzed, wherein the shape of the search window comprises a square shape, a cross shape, a circular shape or an X shape;
the point-by-point processing device is respectively connected with the noise distribution judging device, the uniformity judging device and the data matching device, and is used for receiving the search window and executing the following actions on each pixel point in the sharpened image: taking each pixel point in the sharpened image as a pixel point to be processed, acquiring each pixel point in a search window taking the pixel point to be processed as a centroid position in the sharpened image as each adjacent pixel point, sequencing each pixel value of each adjacent pixel point, deleting a plurality of maximum pixel values with the same number as the removal number, also deleting a plurality of minimum pixel values with the same number as the removal number, and performing averaging calculation on the remaining plurality of pixel values to obtain a processed pixel value of the pixel point to be processed;
the median filtering equipment is connected with the point-by-point processing equipment and used for receiving the point-by-point processed images and executing median filtering processing on the point-by-point processed images so as to obtain and output corresponding median filtering images;
the subimage extraction device is connected with the median filtering device and used for receiving the point-by-point processing image and the median filtering image, performing subimage segmentation processing on the point-by-point processing image based on a preset segmentation size to obtain a plurality of first subimages, and performing subimage segmentation processing on the median filtering image based on the preset segmentation size to obtain a plurality of second subimages;
the representative processing device is connected with the sub-image extraction device and used for averaging a plurality of bipolar pulse noise amplitudes of a plurality of first sub-images at preset positions in the point-by-point processed image to obtain pre-processed bipolar pulse noise amplitudes and averaging a plurality of bipolar pulse noise amplitudes of a plurality of second sub-images at preset positions in the median filtered image to obtain post-processed bipolar pulse noise amplitudes;
the noise elimination device is connected with the representative processing device and used for executing circulating median filtering processing on the median filtering image when the received processed bipolar pulse noise amplitude is larger than a preset bipolar pulse noise amplitude until the bipolar pulse noise amplitude of the obtained processed image does not exceed the preset bipolar pulse noise amplitude, and outputting the obtained processed image as a noise elimination image;
the characteristic identification device is connected with the noise elimination device and used for receiving the noise elimination image, identifying each human body area in the noise elimination image based on human body imaging characteristics, and matching one or more dinner plate areas from the noise elimination image based on dinner plate patterns of fast food restaurants;
in the distribution detection equipment, the preset distance is marked by the number of pixel points, and the distance from the human body area to the adjacent dinner plate area is the number of pixel points from the centroid of the human body area to the centroid of the adjacent dinner plate area;
in the distribution detection device, the determining that there is no dinner plate region within a preset distance around a certain human body region in the noise-removed image includes: the distance from a certain human body area to each of the adjacent dinner plate areas is larger than the preset distance.
Next, a specific configuration of the network disconnection managing and controlling device of the present invention will be further described.
In the network cut-off management and control device:
and the noise elimination equipment is also used for taking the median filtering image as a noise elimination image and outputting the noise elimination image when the received processed bipolar pulse noise amplitude is less than or equal to a preset bipolar pulse noise amplitude.
In the network cut-off management and control device:
the noise elimination device further comprises an amplitude receiving sub-device, a cyclic processing sub-device and an image output sub-device, wherein the cyclic processing sub-device is respectively connected with the amplitude receiving sub-device and the image output sub-device.
In the network cut-off management and control device:
and the circular processing sub-device is used for executing circular median filtering processing on the median filtering image when the received bipolar pulse noise amplitude after processing is larger than a preset bipolar pulse noise amplitude until the bipolar pulse noise amplitude of the obtained processed image does not exceed the preset bipolar pulse noise amplitude.
In the network cut-off management and control device:
in the representative processing apparatus, the preset position is a central position of a processed image, that is, for the median filtered image or the point-by-point processed image, the preset position is a central position of the median filtered image or the point-by-point processed image.
In the network cut-off management and control device:
the point-by-point processing equipment is further used for acquiring a point-by-point processed image corresponding to the sharpened image based on each processed pixel value of each pixel point in the sharpened image.
In the network cut-off management and control device:
the sub-image segmentation device includes an image analysis sub-device, a position analysis sub-device, and a sub-image output sub-device.
In the network cut-off management and control device:
the preset parameter threshold range is composed of a preset parameter upper threshold and a preset parameter lower threshold, and the preset parameter upper threshold is larger than the preset parameter lower threshold.
In the network cut-off management and control device:
in the data matching apparatus, determining a search window that best matches the sub-image to be analyzed based on the shape of the sub-image to be analyzed includes: the number of pixels occupied by the search window is more than three times the number of the pixels removed.
In the network cut-off management and control device:
in the sub-image segmentation device, the position analysis sub-device is connected to the image analysis sub-device and the sub-image output sub-device, respectively.
In addition, the image analysis sub-device, the position analysis sub-device and the sub-image output sub-device are respectively realized by adopting CPLD chips with different models.
The Complex Programmable Logic Device (CPLD) is a Device developed from PAL and GAL devices, and is relatively large in scale and Complex in structure, and belongs to the field of large-scale integrated circuits. The digital integrated circuit is a digital integrated circuit which is used by a user to construct logic functions according to respective needs. The basic design method is to generate corresponding target files by means of an integrated development software platform and methods such as schematic diagrams, hardware description languages and the like, and to transmit codes to a target chip through a download cable (programming in the system) so as to realize the designed digital system. CPLDs are mainly composed of programmable interconnected matrix cells surrounded by programmable logic Macro cells (MC, Macro cells). The MC structure is complex and has a complex I/O unit interconnection structure, and a user can generate a specific circuit structure according to the requirement to complete a certain function. Because the CPLD adopts metal wires with fixed length to interconnect each logic block, the designed logic circuit has time predictability, and the defect of incomplete time sequence prediction of a sectional type interconnection structure is avoided.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. The utility model provides a network cuts off management and control device which characterized in that includes:
the distribution detection equipment is arranged on a side wall of the fast food restaurant, is connected with the feature recognition equipment and is used for carrying out red highlighting marking on a human body area in the noise elimination image so as to show the key attention of non-dining personnel when no dinner plate area exists within a preset distance around the human body area in the noise elimination image;
the network control equipment is arranged on the side wall of the fast food restaurant, is connected with the distribution detection equipment and is used for cutting off the wireless routing equipment of the fast food restaurant when the number of human body areas without any dinner plate area in the preset distance around the noise elimination image exceeds the limit;
the liquid crystal display device is arranged on the side wall of the fast food restaurant, is connected with the distribution detection device and is used for receiving and displaying the noise elimination image subjected to the red highlighting marking;
the on-site camera device is arranged on the wall top of the fast food restaurant and is used for carrying out on-site camera shooting operation on the environment in the fast food restaurant to obtain a corresponding environment image in the restaurant;
the first analysis equipment is positioned near the field camera device, is connected with the field camera device, and is used for measuring the contrast of the environment image in the store and outputting the measurement result as real-time contrast;
the second analysis equipment is respectively connected with the on-site camera device and the first analysis equipment, and is used for receiving the real-time contrast and the in-store environment image, and performing uniform blocking processing on the in-store environment image based on the real-time contrast to obtain a plurality of corresponding sub-images;
a first processing device connected to the second analysis device, configured to receive the plurality of sub-images, and perform the following processing for each sub-image: acquiring R channel values of all pixel points of the subimages, adding the R channel values of all pixel points of the subimages, and outputting the added result as the color parameter of the subimages;
the second processing device is connected with the first processing device and used for receiving each color parameter of each sub-image, sending out a first control signal when all the color parameters fall within a preset parameter threshold range, and sending out a second control signal when the color parameters fall out of the preset parameter threshold range;
the data output equipment is respectively connected with the second processing equipment and the field camera device, and is used for sending focusing prompt information to the field camera device when receiving the first control signal and not sending any information to the field camera device when receiving the second control signal;
the signal analysis equipment is connected with the field camera device and used for receiving the in-store environment image, extracting characteristic quantity related to sharpening processing of the in-store environment image, inputting the extracted characteristic quantity into a data analysis model consisting of an input layer, an output layer and a plurality of hidden layers, and performing data analysis on the characteristic quantity input by the input layer by layer, wherein the output layer is connected with the last hidden layer and used for outputting the result of data analysis of the last hidden layer, and the output quantity type of the output layer is a sharpening processing type;
the adaptive sharpening device is connected with the signal analysis device and is used for receiving the sharpening processing type and carrying out sharpening operation based on the sharpening processing type on the in-store environment image so as to obtain and output a sharpened image;
the uniformity degree judging device is connected with the self-adaptive sharpening device and used for receiving the sharpened image, detecting a plurality of positions of a plurality of impulse noises in the sharpened image from the sharpened image, and determining the uniformity degree of the distribution of the impulse noises in the sharpened image based on the plurality of positions of the plurality of impulse noises in the sharpened image;
the noise distribution judging device is connected with the uniformity degree judging device and used for receiving the uniformity degree and determining the corresponding removal quantity based on the uniformity degree, wherein the larger the uniformity degree is, the more uniform the pulse noise distribution in the sharpened image is, and the smaller the corresponding removal quantity determined based on the uniformity degree is;
the subimage segmentation device is connected with the uniformity degree judgment device and used for receiving the sharpened image, extracting a plurality of target subimages in which a plurality of targets are respectively positioned from the sharpened image, determining the number of pulse noises in each target subimage based on a plurality of positions of a plurality of pulse noises in the sharpened image, and outputting the target subimage with the largest number of pulse noises as the subimage to be analyzed;
the data matching device is connected with the subimage segmentation device and used for receiving the subimage to be analyzed and determining a search window which is most matched with the subimage to be analyzed based on the shape of the subimage to be analyzed, wherein the shape of the search window comprises a square shape, a cross shape, a circular shape or an X shape;
the point-by-point processing device is respectively connected with the noise distribution judging device, the uniformity judging device and the data matching device, and is used for receiving the search window and executing the following actions on each pixel point in the sharpened image: taking each pixel point in the sharpened image as a pixel point to be processed, acquiring each pixel point in a search window taking the pixel point to be processed as a centroid position in the sharpened image as each adjacent pixel point, sequencing each pixel value of each adjacent pixel point, deleting a plurality of maximum pixel values with the same number as the removal number, also deleting a plurality of minimum pixel values with the same number as the removal number, and performing averaging calculation on the remaining plurality of pixel values to obtain a processed pixel value of the pixel point to be processed;
the median filtering equipment is connected with the point-by-point processing equipment and used for receiving the point-by-point processed images and executing median filtering processing on the point-by-point processed images so as to obtain and output corresponding median filtering images;
the subimage extraction device is connected with the median filtering device and used for receiving the point-by-point processing image and the median filtering image, performing subimage segmentation processing on the point-by-point processing image based on a preset segmentation size to obtain a plurality of first subimages, and performing subimage segmentation processing on the median filtering image based on the preset segmentation size to obtain a plurality of second subimages;
the representative processing device is connected with the sub-image extraction device and used for averaging a plurality of bipolar pulse noise amplitudes of a plurality of first sub-images at preset positions in the point-by-point processed image to obtain pre-processed bipolar pulse noise amplitudes and averaging a plurality of bipolar pulse noise amplitudes of a plurality of second sub-images at preset positions in the median filtered image to obtain post-processed bipolar pulse noise amplitudes;
the noise elimination device is connected with the representative processing device and used for executing circulating median filtering processing on the median filtering image when the received processed bipolar pulse noise amplitude is larger than a preset bipolar pulse noise amplitude until the bipolar pulse noise amplitude of the obtained processed image does not exceed the preset bipolar pulse noise amplitude, and outputting the obtained processed image as a noise elimination image;
the characteristic identification device is connected with the noise elimination device and used for receiving the noise elimination image, identifying each human body area in the noise elimination image based on human body imaging characteristics, and matching one or more dinner plate areas from the noise elimination image based on dinner plate patterns of fast food restaurants;
in the distribution detection equipment, the preset distance is marked by the number of pixel points, and the distance from the human body area to the adjacent dinner plate area is the number of pixel points from the centroid of the human body area to the centroid of the adjacent dinner plate area;
in the distribution detection device, the determining that there is no dinner plate region within a preset distance around a certain human body region in the noise-removed image includes: the distance from a certain human body area to each nearby dinner plate area is greater than the preset distance;
the preset parameter threshold range is composed of a preset parameter upper threshold and a preset parameter lower threshold, and the preset parameter upper threshold is larger than the preset parameter lower threshold.
2. The network disconnection management and control apparatus according to claim 1, wherein:
and the noise elimination equipment is also used for taking the median filtering image as a noise elimination image and outputting the noise elimination image when the received processed bipolar pulse noise amplitude is less than or equal to a preset bipolar pulse noise amplitude.
3. The network disconnection management and control apparatus according to claim 2, wherein:
the noise elimination device further comprises an amplitude receiving sub-device, a cyclic processing sub-device and an image output sub-device, wherein the cyclic processing sub-device is respectively connected with the amplitude receiving sub-device and the image output sub-device.
4. The network disconnection management and control apparatus according to claim 3, wherein:
and the circular processing sub-device is used for executing circular median filtering processing on the median filtering image when the received bipolar pulse noise amplitude after processing is larger than a preset bipolar pulse noise amplitude until the bipolar pulse noise amplitude of the obtained processed image does not exceed the preset bipolar pulse noise amplitude.
5. The network disconnection management and control apparatus according to claim 4, wherein:
in the representative processing apparatus, the preset position is a central position of a processed image, that is, for the median filtered image or the point-by-point processed image, the preset position is a central position of the median filtered image or the point-by-point processed image.
6. The network disconnection management and control apparatus according to claim 5, wherein:
the point-by-point processing equipment is further used for acquiring a point-by-point processed image corresponding to the sharpened image based on each processed pixel value of each pixel point in the sharpened image.
7. The network disconnection management and control apparatus according to claim 6, wherein:
the sub-image segmentation device includes an image analysis sub-device, a position analysis sub-device, and a sub-image output sub-device.
8. The network disconnection management and control apparatus according to claim 7, wherein:
in the data matching apparatus, determining a search window that best matches the sub-image to be analyzed based on the shape of the sub-image to be analyzed includes: the number of pixels occupied by the search window is more than three times the number of the pixels removed.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101303727A (en) * | 2008-07-08 | 2008-11-12 | 北京中星微电子有限公司 | Intelligent management method based on video human number Stat. and system thereof |
| CN101325690A (en) * | 2007-06-12 | 2008-12-17 | 上海正电科技发展有限公司 | Method and system for detecting human flow analysis and crowd accumulation process of monitoring video flow |
| CN104156942A (en) * | 2014-07-02 | 2014-11-19 | 华南理工大学 | Detection method for remnants in complex environment |
| CN107330735A (en) * | 2017-07-04 | 2017-11-07 | 百度在线网络技术（北京）有限公司 | Method and apparatus for determining association shops |
| CN108305187A (en) * | 2018-01-19 | 2018-07-20 | 中国计量大学 | Dining room based on computer vision is had dinner guidance system |
| CN109118681A (en) * | 2018-08-22 | 2019-01-01 | 明超 | Dining room service plate vegetable is had a prescription priced mechanism |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080262897A1 (en) * | 2007-04-17 | 2008-10-23 | Embarq Holdings Company, Llc | System and method for geographic location of customer services |
| WO2012015237A2 (en) * | 2010-07-27 | 2012-02-02 | 에스케이텔레콤 주식회사 | Location and state information providing/inquiring system using near field communication, log information providing/inquiring system and method, service server and customer terminal, location and state providing/inquiring method |
| US20170109843A1 (en) * | 2015-10-20 | 2017-04-20 | Back Home Foods LLC | System and method for mobile-assisted digital waiter |
| WO2019113777A1 (en) * | 2017-12-12 | 2019-06-20 | 福建联迪商用设备有限公司 | Commodity information display method, mobile terminal, and commodity information display system |
-
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101325690A (en) * | 2007-06-12 | 2008-12-17 | 上海正电科技发展有限公司 | Method and system for detecting human flow analysis and crowd accumulation process of monitoring video flow |
| CN101303727A (en) * | 2008-07-08 | 2008-11-12 | 北京中星微电子有限公司 | Intelligent management method based on video human number Stat. and system thereof |
| CN104156942A (en) * | 2014-07-02 | 2014-11-19 | 华南理工大学 | Detection method for remnants in complex environment |
| CN107330735A (en) * | 2017-07-04 | 2017-11-07 | 百度在线网络技术（北京）有限公司 | Method and apparatus for determining association shops |
| CN108305187A (en) * | 2018-01-19 | 2018-07-20 | 中国计量大学 | Dining room based on computer vision is had dinner guidance system |
| CN109118681A (en) * | 2018-08-22 | 2019-01-01 | 明超 | Dining room service plate vegetable is had a prescription priced mechanism |
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