Features and Boundaries
Master computer vision techniques to detect and analyze features and boundaries in digital images for accurate object recognition.
Master computer vision techniques to detect and analyze features and boundaries in digital images for accurate object recognition.
This comprehensive course explores fundamental techniques in computer vision for detecting features and boundaries in images. Students learn essential methods for edge and corner detection, boundary identification, and feature extraction using advanced algorithms like SIFT. The course covers practical applications including image stitching, face detection, and object recognition, providing both theoretical understanding and hands-on implementation experience.
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What you'll learn
Master edge and corner detection techniques
Implement active contours for complex boundaries
Understand and apply the Hough Transform
Develop SIFT-based feature detection skills
Create image stitching applications
Implement face detection algorithms
Skills you'll gain
This course includes:
4.8 Hours PreRecorded video
29 assignments
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FullTime access
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There are 6 modules in this course
This course provides a comprehensive introduction to feature and boundary detection in computer vision. The curriculum progresses from fundamental concepts of edge and corner detection to advanced topics like SIFT detection and face recognition. Students learn through theoretical lectures and practical implementations, covering essential algorithms and techniques used in modern computer vision applications.
Getting Started: Features and Boundaries
Module 1 · 2 Hours to complete
Edge Detection
Module 2 · 4 Hours to complete
Boundary Detection
Module 3 · 4 Hours to complete
SIFT Detector
Module 4 · 4 Hours to complete
Image Stitching
Module 5 · 3 Hours to complete
Face Detection
Module 6 · 4 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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