Shree Nayar

Shree K. Nayar is the T. C. Chang Professor of Computer Science at Columbia University. He heads the Columbia Vision Laboratory (CAVE), which develops computational imaging and computer vision systems. His research is focused on three areas - the creation of novel cameras that provide new forms of visual information, the design of physics-based models for vision and graphics, and the development of algorithms for understanding scenes from images. His work is motivated by applications in the fields of imaging, computer vision, robotics, virtual reality, augmented reality, visual communication, computer graphics and human-computer interfaces.

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Camera and Imaging (Coursera) Coursera
Columbia University

Camera and Imaging (Coursera)

Embark on a journey through the fascinating world of Camera and Imaging with this expert-led online course. From the historical roots to cutting-edge innovations, you'll explore how imaging technology has evolved over centuries and its profound impact on both photography and computer vision systems. This course is perfect for enthusiasts, students, and professionals looking to deepen their understanding of imaging fundamentals and recent breakthroughs.

Jun 1st 2026
5-12 Weeks
Features and Boundaries (Coursera) Coursera
Columbia University

Features and Boundaries (Coursera)

Explore the critical skill of detecting features and boundaries within images to enhance your understanding and application of vision tasks such as object detection and recognition. This course provides a thorough introduction to various methods used in feature and boundary detection, equipping you with the tools needed for advanced image analysis.

Jun 1st 2026
5-12 Weeks
3D Reconstruction - Multiple Viewpoints (Coursera) Coursera
Columbia University

3D Reconstruction - Multiple Viewpoints (Coursera)

Explore the fascinating world of 3D Reconstruction through Multiple Viewpoints on Coursera. This course dives into the fundamentals of capturing and analyzing 3D scenes using images from various angles. From understanding camera models and calibrating your equipment to mastering simple binocular stereo methods, you'll gain a deep understanding of how to visualize and interpret three-dimensional spaces.

Jun 1st 2026
5-12 Weeks
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