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First Principles of Computer Vision
Добавлен 28 фев 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.
Просмотров: 4 570
Видео
Course 4 | Overview
Просмотров 1,3 тыс.2 года назад
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
Просмотров 9282 года назад
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
Просмотров 1,4 тыс.2 года назад
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
Просмотров 3,8 тыс.2 года назад
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
Просмотров 9 тыс.3 года назад
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
Просмотров 18 тыс.3 года назад
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
Просмотров 34 тыс.3 года назад
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
Просмотров 7 тыс.3 года назад
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
Просмотров 15 тыс.3 года назад
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
Просмотров 30 тыс.3 года назад
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
Просмотров 20 тыс.3 года назад
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
Просмотров 68 тыс.3 года назад
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
Просмотров 22 тыс.3 года назад
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
Просмотров 7 тыс.3 года назад
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
Просмотров 5 тыс.3 года назад
Parametric Appearance Representation | Appearance Matching
Finding Principal Components | Appearance Matching
Просмотров 8 тыс.3 года назад
Finding Principal Components | Appearance Matching
Principal Component Analysis | Appearance Matching
Просмотров 16 тыс.3 года назад
Principal Component Analysis | Appearance Matching
Shape vs. Appearance | Appearance Matching
Просмотров 6 тыс.3 года назад
Shape vs. Appearance | Appearance Matching
Learning Appearance | Appearance Matching
Просмотров 4,9 тыс.3 года назад
Learning Appearance | Appearance Matching
Graph Based Segmentation | Image Segmentation
Просмотров 39 тыс.3 года назад
Graph Based Segmentation | Image Segmentation
Mean-Shift Segmentation | Image Segmentation
Просмотров 39 тыс.3 года назад
Mean-Shift Segmentation | Image Segmentation
k-Means Segmentation | Image Segmentation
Просмотров 34 тыс.3 года назад
k-Means Segmentation | Image Segmentation
Segmentation as Clustering | Image Segmentation
Просмотров 21 тыс.3 года назад
Segmentation as Clustering | Image Segmentation
Segmentation by humans | Image Segmentation
Просмотров 20 тыс.3 года назад
Segmentation by humans | Image Segmentation
Tracking by Feature Detection | Object Tracking
Просмотров 16 тыс.3 года назад
Tracking by Feature Detection | Object Tracking
Gaussian Mixture Model | Object Tracking
Просмотров 31 тыс.3 года назад
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😀