Visual Perception
Master computer vision fundamentals through practical implementation of object tracking, segmentation, and recognition systems for various applications.
Master computer vision fundamentals through practical implementation of object tracking, segmentation, and recognition systems for various applications.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full First Principles of Computer Vision Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.6
(29 ratings)
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What you'll learn
Design algorithms for detecting scene changes
Develop object tracking systems for video analysis
Master image segmentation techniques
Implement appearance-based object recognition
Create neural networks for visual perception
Skills you'll gain
This course includes:
5.8 Hours PreRecorded video
28 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course explores fundamental concepts in computer vision perception. Students learn essential techniques for object tracking in complex scenes, including change detection and feature-based tracking methods. The curriculum covers image segmentation using various approaches like k-means and graph-based methods. Advanced topics include appearance matching using principal component analysis and neural network implementation for object recognition. The course combines theoretical foundations with practical applications in machine learning and computer vision.
Getting Started: Visual Perception
Module 1 · 2 Hours to complete
Object Tracking
Module 2 · 13 Hours to complete
Image Segmentation
Module 3 · 16 Hours to complete
Appearance Matching
Module 4 · 23 Hours to complete
Neural Networks
Module 5 · 27 Hours to complete
Fee Structure
Instructor
Pioneer in Computational Imaging and Professor at Columbia University
Dr. Shree K. Nayar is the T. C. Chang Professor of Computer Science at Columbia University, where he leads the Columbia Vision Laboratory (CAVE). His research focuses on computational imaging and computer vision, with key interests in developing novel camera systems, physics-based models for vision and graphics, and algorithms for scene understanding from images. Dr. Nayar's work has significant applications across various fields, including robotics, virtual reality, augmented reality, and human-computer interfaces.He holds a B.E. in Electrical Engineering from the Birla Institute of Technology, an M.S. in Electrical and Computer Engineering from North Carolina State University, and a Ph.D. from Carnegie Mellon University. Throughout his career, Dr. Nayar has received numerous accolades for his contributions to the field, including the 2010 ACM Software Systems Award for his work on the GroupLens Recommender System. He has published over 300 scientific papers and holds more than 80 patents related to imaging technologies.Dr. Nayar teaches several courses on Coursera, including "3D Reconstruction - Multiple Viewpoints" and "Camera and Imaging," aimed at providing students with foundational knowledge in computer vision and imaging systems.
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4.6 course rating
29 ratings
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