Part I: Fundamentals In Computer Vision
It is fitting that we start with some of the more fundamental concepts in computer vision. The range of topics covered in Part I is wide: camera calibration, structure from motion, dense stereo, 3D modeling, robust techniques for model fitting, and a more recently developed concept called tensor voting.
In Chapter 2, Zhang reviews the different techniques for calibrating a camera. More specifically, he describes calibration techniques that use 3D reference objects, 2D planes, and 1D lines, as well as self-calibration techniques.
One of the more popular (and difficult) areas in computer vision is stereo. Heyden and Pollefeys describe how camera motion and scene structure can be reliably extracted from image ...
Get Emerging Topics in Computer Vision now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
