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 sequences in Chapter 3. Once this is accomplished, dense depth distributions can be extracted for 3D surface reconstruction and image-based rendering applications.

A basic task in computer vision is hypothesizing models (e.g., 2D shapes) and using input data (typically image data) to corroborate and fit the models. In practice, however, robust techniques for model fitting must be used to handle input noise. In Chapter 4, Meer describes various robust regression techniques such as M-estimators, RANSAC, and Hough transform. He also covers the mean shift algorithm for the location estimation problem.

The claim by Medioni and his colleagues that computer vision problems can be addressed within a Gestalt framework is the basis of their work on tensor voting. In Chapter 5, Medioni and Mordohai provide an introduction to the concept of tensor voting, which is a form of binning according to proximity to ideal primitives such as edges and points. They show how this scheme can be applied to a variety of applications, such as curve and surface extraction from noisy 2D and 3D points (respectively), stereo matching, and motion-based grouping.