Computing power of general notebook graphics card
Publish: 2021-05-26 03:01:24
1. Computer vision is a science that studies how to make the machine "see". Furthermore, it refers to the use of cameras and computers instead of human eyes to recognize, track and measure the target, and further do graphics processing, so that the computer processing becomes more suitable for human eyes to observe or transmit to the instrument for detection. As a scientific discipline, computer vision studies related theories and technologies, trying to build an artificial intelligence system that can obtain "information" from images or multidimensional data. The information here refers to the information defined by Shannon, which can be used to help make a "decision". Because perception can be seen as extracting information from sensory signals, computer vision can also be seen as the science of how to make artificial systems "perceive" from images or multidimensional data<
Chinese name
Computer Vision
foreign name
Computer Vision
fast
navigation
analysis of principle related status quo, application similarities and differences, system elements Conference Journal
definition
Computer vision is a simulation of biological vision using computers and related equipment. Its main task is to get the 3D information of the corresponding scene by processing the collected pictures or videos, just as humans and many other creatures do every day
computer vision is a knowledge about how to use the camera and computer to obtain the data and information we need. Figuratively speaking, it is to install eyes (camera) and brain (algorithm) on the computer, so that the computer can perceive the environment. We Chinese Idioms & quot; Seeing is believing & quot; And what Westerners often say & quot; One picture is worth ten thousand words" It expresses the importance of vision to human beings. It's not hard to imagine how broad the application prospects of machines with vision can be
computer vision is a challenging and important research field in both engineering and science. Computer vision is a comprehensive subject, it has attracted researchers from various disciplines to participate in its research. These include computer science and engineering, signal processing, physics, applied mathematics and statistics, neurophysiology and cognitive science.
Chinese name
Computer Vision
foreign name
Computer Vision
fast
navigation
analysis of principle related status quo, application similarities and differences, system elements Conference Journal
definition
Computer vision is a simulation of biological vision using computers and related equipment. Its main task is to get the 3D information of the corresponding scene by processing the collected pictures or videos, just as humans and many other creatures do every day
computer vision is a knowledge about how to use the camera and computer to obtain the data and information we need. Figuratively speaking, it is to install eyes (camera) and brain (algorithm) on the computer, so that the computer can perceive the environment. We Chinese Idioms & quot; Seeing is believing & quot; And what Westerners often say & quot; One picture is worth ten thousand words" It expresses the importance of vision to human beings. It's not hard to imagine how broad the application prospects of machines with vision can be
computer vision is a challenging and important research field in both engineering and science. Computer vision is a comprehensive subject, it has attracted researchers from various disciplines to participate in its research. These include computer science and engineering, signal processing, physics, applied mathematics and statistics, neurophysiology and cognitive science.
2. Can you say it more popular? I don't understand what visual direction means? VR
3. Computational vision doesn't require mathematics, right? I do computer vision. I feel like I'm engaged in mathematics. I struggle with formulas all day. But it's not pure theory, it can make practical things
reading PhD is a good direction.
reading PhD is a good direction.
4. First see which school you want to test! They are out of the question! When it's settled, we'll find a way to solve the real problems of the past years. Find a few more to test together! Postgraate entrance examination is also an exam, public course scores should be sufficient, professional courses are likely to be bullied by other people's own examinees~
5. As an algorithm engineer in this field, I mainly work in intelligent transportation, security, monitoring, instrial detection, OCR and other fields. My new master's degree is about 7K in Beijing. It varies from person to person, and I have high requirements for basic mathematics
6. Computer vision and machine vision, first of all, the application scene is not the same, as far as the image Zhao Xu replied: you put the camera to people is CV, facing the workshop is MV
the application scenarios of computer vision and machine vision are different, just like pulling freight cars and carrying passenger cars. Yes, the emphasis is different. One focuses on the branch of artificial intelligence, and the other focuses on instrial applications! In short, computer vision focuses on deep learning and software, while machine vision focuses on feature recognition and has higher requirements on hardware. However, with the development of intelligent recognition, these two directions will penetrate and integrate with each other, and the difference is only limited to different application fields
secondly, I feel that the biggest difference lies in the different emphasis of technical requirements, or even great differences
computer vision is mainly about qualitative analysis, such as classification recognition. This is a cup and that is a dog. Or do identification, such as face recognition, license plate recognition. Or do behavior analysis, such as intrusion, wandering, leftovers, crowd gathering, etc
machine vision mainly focuses on quantity analysis, such as measuring the diameter of a part through vision. Generally speaking, it requires high accuracy. I remember meeting a requirement before: visual measurement of railway turnout gap. When I just graated, I worked in the railway, worked in the control system, and also drove a diesel locomotive. I know the importance of the switch gap very well. If you can't measure this thing, ha ha:)
of course, it can't be done according to the quality or quantity. Some computer vision applications also need to analyze the quantity, such as the number of people in shopping malls. Some machine vision also need to analyze quality, such as automatic sorting of parts. But, generally speaking, the requirement of computer vision for quantity is not very high. The statistical error of the number of people in shopping malls can't kill people, but machine vision really can, such as the measurement of the turnout gap
since the requirements are so high, is machine vision more difficult than computer vision? No, it should be said that each has its own difficulties
the application scene of computer vision is relatively complex, and there are many types of objects to be recognized, with irregular shape and weak regularity. Sometimes it is difficult to use objective quantity as the basis of recognition, such as age and gender. So deep learning is more suitable for computer vision. And the light, distance, angle and other prerequisites are often dynamic, so the accuracy requirements are generally lower
machine vision is just the opposite, the scene is relatively simple and fixed, there are few types of recognition (in the same application), rules and rules, but the requirements for accuracy and processing speed are relatively high. As for speed, the resolution of general machine vision is much higher than that of computer vision, and it often requires real-time, so the processing speed is very important, and it is not suitable for deep learning at present
the above discussion is about technology and business. The application of computer vision is wider. After all, many businesses are related to people, such as face recognition and behavior analysis. Many vertical fields have potential demands for computer vision, which is relatively more suitable for entrepreneurship
as the name suggests, machine vision business is mainly related to machines, and it has high requirements for accuracy and even safety, which means it has a high threshold in terms of qualified brands. Therefore, oligopoly is serious. Generally speaking, it is more suitable for working than starting a business<
machine vision (MV) & Computer Vision (CV)
in terms of subject classification, both of them are considered to be subordinate subjects of artificial intelligence.
there are several branches:
one is image processing, mainly signal and system, statistics and optimization; the other is solving the relationship between scenery and image, For example, stereo vision, three-dimensional reconstruction, mainly geometry
one is pattern recognition, such as how to segment the image and identify the target, mainly artificial intelligence
but in practice, the subjective feeling
MV pays more attention to the application of generalized image signal (laser, camera) and automatic control (proction line)
CV pays more attention to the research of (2D, 3D) image signal itself and interdisciplinary research related to image (medical image analysis, map navigation).
the application scenarios of computer vision and machine vision are different, just like pulling freight cars and carrying passenger cars. Yes, the emphasis is different. One focuses on the branch of artificial intelligence, and the other focuses on instrial applications! In short, computer vision focuses on deep learning and software, while machine vision focuses on feature recognition and has higher requirements on hardware. However, with the development of intelligent recognition, these two directions will penetrate and integrate with each other, and the difference is only limited to different application fields
secondly, I feel that the biggest difference lies in the different emphasis of technical requirements, or even great differences
computer vision is mainly about qualitative analysis, such as classification recognition. This is a cup and that is a dog. Or do identification, such as face recognition, license plate recognition. Or do behavior analysis, such as intrusion, wandering, leftovers, crowd gathering, etc
machine vision mainly focuses on quantity analysis, such as measuring the diameter of a part through vision. Generally speaking, it requires high accuracy. I remember meeting a requirement before: visual measurement of railway turnout gap. When I just graated, I worked in the railway, worked in the control system, and also drove a diesel locomotive. I know the importance of the switch gap very well. If you can't measure this thing, ha ha:)
of course, it can't be done according to the quality or quantity. Some computer vision applications also need to analyze the quantity, such as the number of people in shopping malls. Some machine vision also need to analyze quality, such as automatic sorting of parts. But, generally speaking, the requirement of computer vision for quantity is not very high. The statistical error of the number of people in shopping malls can't kill people, but machine vision really can, such as the measurement of the turnout gap
since the requirements are so high, is machine vision more difficult than computer vision? No, it should be said that each has its own difficulties
the application scene of computer vision is relatively complex, and there are many types of objects to be recognized, with irregular shape and weak regularity. Sometimes it is difficult to use objective quantity as the basis of recognition, such as age and gender. So deep learning is more suitable for computer vision. And the light, distance, angle and other prerequisites are often dynamic, so the accuracy requirements are generally lower
machine vision is just the opposite, the scene is relatively simple and fixed, there are few types of recognition (in the same application), rules and rules, but the requirements for accuracy and processing speed are relatively high. As for speed, the resolution of general machine vision is much higher than that of computer vision, and it often requires real-time, so the processing speed is very important, and it is not suitable for deep learning at present
the above discussion is about technology and business. The application of computer vision is wider. After all, many businesses are related to people, such as face recognition and behavior analysis. Many vertical fields have potential demands for computer vision, which is relatively more suitable for entrepreneurship
as the name suggests, machine vision business is mainly related to machines, and it has high requirements for accuracy and even safety, which means it has a high threshold in terms of qualified brands. Therefore, oligopoly is serious. Generally speaking, it is more suitable for working than starting a business<
machine vision (MV) & Computer Vision (CV)
in terms of subject classification, both of them are considered to be subordinate subjects of artificial intelligence.
there are several branches:
one is image processing, mainly signal and system, statistics and optimization; the other is solving the relationship between scenery and image, For example, stereo vision, three-dimensional reconstruction, mainly geometry
one is pattern recognition, such as how to segment the image and identify the target, mainly artificial intelligence
but in practice, the subjective feeling
MV pays more attention to the application of generalized image signal (laser, camera) and automatic control (proction line)
CV pays more attention to the research of (2D, 3D) image signal itself and interdisciplinary research related to image (medical image analysis, map navigation).
7. Do not do large-scale computing, the general machine on the line, if the large-scale use GPU
8. If you drive for about 30 minutes, if you take Skytrain, take the Canada line, which starts from the airport waterfront station to Yaletown Roundhouse station for about 35 minutes. It's almost the same time, because there will be a bit of traffic jam when driving.
9. first. There are many delta hotels, but if you say it's downtown Vancouver, you should take Canada line to the airport. The subway charges by zone. 1 zone 2.75 2 4.23 more than 5 yuan, but if you don't understand, it is recommended to buy daypass directly. In a day, Skytrain and bus are free. It's about 40 minutes - 1 hour and 20 minutes from downtown Vancouver to the airport, because you didn't say the exact location, so I can only give you a rough idea.
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