Features and Boundaries
Learn the fundamental concepts of feature and boundary detection in images, as well as their application in computer vision tasks.
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Course Description
- Master feature and boundary detection. Develop expertise in detecting features and boundaries within images, honing essential skills for tasks like object recognition, detection, and metrology.
- Engage with comprehensive vision techniques. Explore diverse methods for detecting features and boundaries, understanding their significance in solving critical vision challenges.
- Progress from edges to interest points. Begin by grasping the basics, detecting simple features like edges and corners, before delving into the concept of "interest points" and their detection through the SIFT detector.
- Uncover face detection and beyond. Conclude the course by delving into the realm of face detection, uncovering its various applications and expanding your understanding of advanced vision concepts.
Course Prerequisites
- Proficiency in the fundamentals of linear algebra and calculus
- Familiarity with any programming language will aid in understanding software implementation of course methods
- Prior experience in imaging or computer vision is not a prerequisite
What You Will Learn
By the end of this course, learners will be able to:
Expertly Detect Features and Boundaries: Acquire the skill to proficiently identify features and boundaries within images, a crucial capability in the realm of computer vision and related domains.
Master a Range of Detection Techniques: Develop an in-depth understanding of detecting diverse elements like edges, corners, lines, curves, active contours, and basic parametric shapes in images.
Implement Advanced Detection Tools: Gain practical knowledge in the application of the SIFT detector, as well as the estimation of image transformations, homography, image stitching, and the complex process of face detection using Haar features and support vector machines.
Apply Hands-on Problem Solving: Through immersive practical examples and real-world applications, acquire hands-on experience in tackling intricate vision challenges, enhancing your ability to solve complex tasks effectively.
Course Outline
Module 1: Introduction to first principles of computer vision
Module 2: Edge detection
Module 3: Boundary detection
Module 4: SIFT detector
Module 5: Image stitching
Module 6: Face detection
Instructors
Shree K. Nayar is the T.C. Chang Professor of Computer Science at Columbia University and leads the Columbia Vision Laboratory. His research focuses on developing advanced computer vision systems for digital imaging, computer graphics, robotics, and human-computer interfaces. He has received numerous awards and honors for his research and teaching, including the David Marr Prize, the David and Lucile Packard Fellowship, and the National Young Investigator Award. Nayar has also been elected to the National Academy of Engineering, the American Academy of Arts and Sciences, and the National Academy of Inventors. He holds a BS degree in Electrical Engineering from Birla Institute of Technology, an MS degree in Electrical and Computer Engineering from North Carolina State University, and a PhD degree in Electrical and Computer Engineering from the Robotics Institute at Carnegie Mellon University.
Please note that there are no instructors or course assistants actively monitoring this course.