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First Principles of Computer Vision
Приєднався 28 лют 2021
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners, and enthusiasts who have no prior knowledge of computer vision.
Course 5 | Overview
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and enthusiasts who have no prior knowledge of computer vision.
Переглядів: 4 570
Відео
Course 4 | Overview
Переглядів 1,3 тис.2 роки тому
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and en...
Course 3 | Overview
Переглядів 9282 роки тому
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and en...
Course 2 | Overview
Переглядів 1,4 тис.2 роки тому
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and en...
Course 1 | Overview
Переглядів 3,8 тис.2 роки тому
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and en...
When to Use Machine Learning? | Neural Networks
Переглядів 9 тис.3 роки тому
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and en...
Gradient Descent | Neural Networks
Переглядів 18 тис.3 роки тому
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and en...
Backpropagation Algorithm | Neural Networks
Переглядів 34 тис.3 роки тому
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and en...
Example Applications | Neural Networks
Переглядів 7 тис.3 роки тому
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and en...
Neural Network | Neural Networks
Переглядів 15 тис.3 роки тому
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and en...
Activation Function | Neural Networks
Переглядів 30 тис.3 роки тому
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and en...
Perceptron Network | Neural Networks
Переглядів 20 тис.3 роки тому
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and en...
Perceptron | Neural Networks
Переглядів 68 тис.3 роки тому
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and en...
Overview | Neural Networks
Переглядів 22 тис.3 роки тому
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and en...
Appearance Matching | Appearance Matching
Переглядів 7 тис.3 роки тому
First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and en...
Parametric Appearance Representation | Appearance Matching
Переглядів 5 тис.3 роки тому
Parametric Appearance Representation | Appearance Matching
Finding Principal Components | Appearance Matching
Переглядів 8 тис.3 роки тому
Finding Principal Components | Appearance Matching
Principal Component Analysis | Appearance Matching
Переглядів 16 тис.3 роки тому
Principal Component Analysis | Appearance Matching
Shape vs. Appearance | Appearance Matching
Переглядів 6 тис.3 роки тому
Shape vs. Appearance | Appearance Matching
Learning Appearance | Appearance Matching
Переглядів 4,9 тис.3 роки тому
Learning Appearance | Appearance Matching
Graph Based Segmentation | Image Segmentation
Переглядів 39 тис.3 роки тому
Graph Based Segmentation | Image Segmentation
Mean-Shift Segmentation | Image Segmentation
Переглядів 39 тис.3 роки тому
Mean-Shift Segmentation | Image Segmentation
k-Means Segmentation | Image Segmentation
Переглядів 34 тис.3 роки тому
k-Means Segmentation | Image Segmentation
Segmentation as Clustering | Image Segmentation
Переглядів 21 тис.3 роки тому
Segmentation as Clustering | Image Segmentation
Segmentation by humans | Image Segmentation
Переглядів 20 тис.3 роки тому
Segmentation by humans | Image Segmentation
Tracking by Feature Detection | Object Tracking
Переглядів 16 тис.3 роки тому
Tracking by Feature Detection | Object Tracking
Gaussian Mixture Model | Object Tracking
Переглядів 31 тис.3 роки тому
Gaussian Mixture Model | Object Tracking
Beautiful lecture, easy to Follow important and difficult concepts
This is educational gold.
How does the size of the matrix affect the dynamic range?
thank you
Briiliant🎉
I ended here in this amazing channel because recently had an idea of a patent, but the professor Nayar Shree had already registered 20 years ago. 😅
Sir, grateful to you for this wonderful series. The amazing clarity with which you explain is a great eye-opener. You have made a complex subject look so easy to discern. I wish you could do a similar series with equal clarity on the soft-subjects such as light, color and composition. Thank you whole-heartedly yet again. Pranam 🙏🙏
Amazing lecture series. Wish I could comment that on each video, but definitely, definitely, definitely, a winner.
Absolutely amazing content. Thanks 🙏
Tremendous series and prrsented in an understandable way.
This lecturer is just perfect.
In CCD electrons are transported in columns not in rows. Then it reaches serial register. Its a massive difference
This series is fantastic. Very helpful, thank you
This is a totally stunning series on digital imaging. Beautifully paced, clearly articulated, and excellently illustrated. Thank you for making these.
Any cheaper sensors with good IR and UV sensitivity?
Very well explained ! thank you
I had a question:Given that Bmax is the filling up of a potential well, wouldn't that be based on the material being used as the sensor? Then, how can the amount of exposure time possible for a frame in a video determine a low dynamic range of the sensor?
Great lecture series, the main topic to make this series more complete would be ISP. Is there any plans to add ISP lecture series in the future?
You mean complementary, not complimentary.
I can’t believe so much is offered to us. Thankyou
Amazing lecture
Variance of noise is Delta^2/12 @8:40
❤❤❤
Awesome initiative 🫡
Wow.. brilliant.. great for beginners..
This is a fantastic presentation. Thank you very much!
Var should be (Delta*Delta) / 12
Thanks All of you for your hardworking ! 🙏🙏🙏🙏
Min. 13:00, When he says that digital cameras have 72.2 dB today, Is he talking in 2021?
thank you very much!
Amazing explanation!! thanks!
Thank you so much for your videos, brilliant explanations!
Facinating! Thank you
Regarding the dynamic range definition In electronics,when comparing voltage we use 20log(*) & when comparing power we use 10log(*) Why in image processing the dynamic range of energy comparison is using 20log(*) & not 10log(*)?
Great thanks😀
Great😀
Great😊
Thanks a lot😁
Bravo😁
Wow😀