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Understanding computer vision
Understanding computer vision
Computer vision is a science that discusses how to make a machine “see”. Further, it refers to the use of cameras and computers instead of human eyes to stop the recognition, tracking and measurement of machine vision, and further image processing. The computer is used to dispose the image that is more suitable for the human eye to observe or stop the instrument detection.
Learning and computing allow the machine to better understand the picture environment and build a vision system with real intelligence. There is a large amount of picture and video content in the current environment. It is imperative that scholars understand and find ways to find out the details that we have not noticed before. The basic process of computer vision is:
Computer Generates Mathematical Models from Pictures
The computer graphics are drawn off the image in the model, then used as an input during the image processing, and the processing image is output as an output
Seven Steps to Know Computer Vision
The concept of computer vision is in fact partially stacked with many concepts in certain aspects, including: artificial intelligence, digital image processing, machine learning, deep learning, mode recognition, probability map models, scientific calculations, and a series of mathematical calculations. Therefore, you can think of this article as the first step in deepening this category of research. This article will try to cover as much as possible, but there may still be some more complicated topics, and there may be some omissions, please forgive me.
Step 1 - Background
Generally speaking, you should have a little related academic background, such as related courses on probability, statistics, linear algebra, and calculus (differential and integral). It is better to know about matrix calculations. In addition, from the perspective of my experience, assuming you have an understanding of digital signal processing, it will be easier to understand the concept later.
At the completion level, you'd better use one of MATLAB or Python. It's important to remember that computer vision is almost entirely related to computer programming.
Seven Steps to Meet You with Computer Vision Seven Steps to Know Computer Vision
You can also take a course on the Course of Probability Modeling on Coursera. This course is relatively difficult (more in depth), and you can learn about it after some time.
丨 Step 2 - Digital Image Processing
Watch the course taught by Guillermo Sapiro from Duke University - "Image and Video Processing: From Mars to Hollywood Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital". All are independent and contain a lot of exercises. You can find relevant course video information on coursera and YouTube. In addition, you can read "Digital Image Processing" written by Gonzalez and Woods and use MATLAB to run the mentioned examples. Confidence will surely gain.
Seven Steps to Know Computer Vision
丨 Step 3 - Computer Vision
Once learning about digital image processing related content, the next step should be to understand the application of relevant mathematical models in various image and video content. Prof. Mubarak Shah from the University of Florida in Computer Vision can be a good introductory course that covers almost all basic concepts.
Seven Steps to Know Computer Vision
While watching these videos, you can learn the concepts and algorithms used by Gatech's Professor James Hays' computer vision projects. These exercises are also based on MATLAB. Do not skip these exercises. Only deeper understanding of these algorithms and formulas will be required during the actual practice.
Step 4 - Advanced Computer Vision
Suppose you carefully studied the content of the first three steps. You can usually go to advanced computer vision related learning.
Nikos Paragios and Pawan Kumar from the Central Polytechnic Institute in Paris taught a Discrete Inference in Artificial Vision course in artificial vision. It can provide relevant probabilistic graphical models and a great deal of mathematics related to computer vision.
Seven Steps to Know Computer Vision
Looking at this step from time to time is more fun. This course will surely make you feel how complex it is to construct a machine vision system with a simple model. After completing this course, I took a big step before I reached academic papers.
丨 Step 5 - Introducing Python and Open Source Framework
This step we have to come into contact with Python programming language.
In Python there are many related extensions like OpenCV, PIL, vlfeat, which are often the best opportunities to apply these extensions to your project. Assuming there are other open source frameworks, it is not necessary to write everything from scratch.
Assuming that you need reference materials, you can think about programming computer vision with Python using Python. This book is enough. You can try it out and see how MATLAB and Python can accomplish your algorithm.
Step 6 - Machine Learning and CovNets (Convolutional Neural Network)
There is a lot of information on how to learn from scratch, and you can find a lot of related tutorials online.
From the beginning it is best to use Python to stop programming from time to time, you can see the "use Python to establish a machine learning system - Building Machine Learning Systems with Python" and "Python Machine Learning - Python Machine Learning" these two books.
At present, deep learning is in full swing and you can try to learn the application of convolutional neural networks in computer vision. We recommend Stanford's CS231n course: Convolutional neural networks for visual recognition.
Seven Steps to Know Computer Vision
丨 Step 7 - How to go further
At this point, you may feel that you have talked about too much content and that you have learned too much. However, you can further suspend exploration and research.
One way to do this is to look at a series of workshops held by Sanja Fidler and James Hays of the University of Toronto to help you understand the latest concepts in the direction of computer vision.
Another kind of academic paper that follows CVPR, ICCV, ECCV, BMVC and other top academic conferences (also can be concerned about relevant reports of Lei Feng Net), after the seminar, the target speeches, and the tutorial and other schedules will certainly learn a lot knowledge.
Summary: Suppose you follow the steps to complete all your learning tasks step by step. At that time, you will learn about the history of filters, feature detection, drawing, camera models, trackers in computer vision, and learn about segmentation and recognition, neural networks, and The latest pause in deep learning. I hope that this article will help you to go further in the field of computer vision and learn more.
The
点击图标下载 App
Learning and computing allow the machine to better understand the picture environment and build a vision system with real intelligence. There is a large amount of picture and video content in the current environment. It is imperative that scholars understand and find ways to find out the details that we have not noticed before. The basic process of computer vision is:
Computer Generates Mathematical Models from Pictures
The computer graphics are drawn off the image in the model, then used as an input during the image processing, and the processing image is output as an output
Seven Steps to Know Computer Vision
The concept of computer vision is in fact partially stacked with many concepts in certain aspects, including: artificial intelligence, digital image processing, machine learning, deep learning, mode recognition, probability map models, scientific calculations, and a series of mathematical calculations. Therefore, you can think of this article as the first step in deepening this category of research. This article will try to cover as much as possible, but there may still be some more complicated topics, and there may be some omissions, please forgive me.
Step 1 - Background
Generally speaking, you should have a little related academic background, such as related courses on probability, statistics, linear algebra, and calculus (differential and integral). It is better to know about matrix calculations. In addition, from the perspective of my experience, assuming you have an understanding of digital signal processing, it will be easier to understand the concept later.
At the completion level, you'd better use one of MATLAB or Python. It's important to remember that computer vision is almost entirely related to computer programming.
Seven Steps to Meet You with Computer Vision Seven Steps to Know Computer Vision
You can also take a course on the Course of Probability Modeling on Coursera. This course is relatively difficult (more in depth), and you can learn about it after some time.
丨 Step 2 - Digital Image Processing
Watch the course taught by Guillermo Sapiro from Duke University - "Image and Video Processing: From Mars to Hollywood Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital". All are independent and contain a lot of exercises. You can find relevant course video information on coursera and YouTube. In addition, you can read "Digital Image Processing" written by Gonzalez and Woods and use MATLAB to run the mentioned examples. Confidence will surely gain.
Seven Steps to Know Computer Vision
丨 Step 3 - Computer Vision
Once learning about digital image processing related content, the next step should be to understand the application of relevant mathematical models in various image and video content. Prof. Mubarak Shah from the University of Florida in Computer Vision can be a good introductory course that covers almost all basic concepts.
Seven Steps to Know Computer Vision
While watching these videos, you can learn the concepts and algorithms used by Gatech's Professor James Hays' computer vision projects. These exercises are also based on MATLAB. Do not skip these exercises. Only deeper understanding of these algorithms and formulas will be required during the actual practice.
Step 4 - Advanced Computer Vision
Suppose you carefully studied the content of the first three steps. You can usually go to advanced computer vision related learning.
Nikos Paragios and Pawan Kumar from the Central Polytechnic Institute in Paris taught a Discrete Inference in Artificial Vision course in artificial vision. It can provide relevant probabilistic graphical models and a great deal of mathematics related to computer vision.
Seven Steps to Know Computer Vision
Looking at this step from time to time is more fun. This course will surely make you feel how complex it is to construct a machine vision system with a simple model. After completing this course, I took a big step before I reached academic papers.
丨 Step 5 - Introducing Python and Open Source Framework
This step we have to come into contact with Python programming language.
In Python there are many related extensions like OpenCV, PIL, vlfeat, which are often the best opportunities to apply these extensions to your project. Assuming there are other open source frameworks, it is not necessary to write everything from scratch.
Assuming that you need reference materials, you can think about programming computer vision with Python using Python. This book is enough. You can try it out and see how MATLAB and Python can accomplish your algorithm.
Step 6 - Machine Learning and CovNets (Convolutional Neural Network)
There is a lot of information on how to learn from scratch, and you can find a lot of related tutorials online.
From the beginning it is best to use Python to stop programming from time to time, you can see the "use Python to establish a machine learning system - Building Machine Learning Systems with Python" and "Python Machine Learning - Python Machine Learning" these two books.
At present, deep learning is in full swing and you can try to learn the application of convolutional neural networks in computer vision. We recommend Stanford's CS231n course: Convolutional neural networks for visual recognition.
Seven Steps to Know Computer Vision
丨 Step 7 - How to go further
At this point, you may feel that you have talked about too much content and that you have learned too much. However, you can further suspend exploration and research.
One way to do this is to look at a series of workshops held by Sanja Fidler and James Hays of the University of Toronto to help you understand the latest concepts in the direction of computer vision.
Another kind of academic paper that follows CVPR, ICCV, ECCV, BMVC and other top academic conferences (also can be concerned about relevant reports of Lei Feng Net), after the seminar, the target speeches, and the tutorial and other schedules will certainly learn a lot knowledge.
Summary: Suppose you follow the steps to complete all your learning tasks step by step. At that time, you will learn about the history of filters, feature detection, drawing, camera models, trackers in computer vision, and learn about segmentation and recognition, neural networks, and The latest pause in deep learning. I hope that this article will help you to go further in the field of computer vision and learn more.
The
点击图标下载 App