Chapter 1. Introduction

The topics in this book were handpicked to showcase the developments we consider to be exciting and promising in computer vision. They are a mix of more well-known and traditional topics (such as camera calibration, multiview geometry, and face detection) and newer ones (such as vision for special effects and tensor voting framework). All have the common denominator of either demonstrated longevity or potential for endurance in computer vision.

The book is designed to accompany computer vision textbooks. Each chapter is self-contained and is written by well-known authorities in the area. The book is organized into three parts, covering various fundamentals, applications, and programming aspects of computer vision.

Part I, "Fundamentals In Computer Vision," consists of four chapters. Two of the chapters deal with the more conventional but still popular areas: camera calibration and multiview geometry. They deal with the most fundamental operations associated with vision. The chapter on robust estimation techniques will be very useful for researchers and practitioners of computer vision alike. There is also a chapter on a more recently developed tool (the tensor voting framework) that can be customized for a variety of problems.

Part II, "Applications In Computer Vision," covers two more recent applications (image-based lighting and vision for visual effects) and three in more conventional areas (image search engines, face detection and recognition, and perceptual interfaces).

One of the more overlooked areas in computer vision is the programming aspect of computer vision. While generic commercial packages can be used, there exist popular libraries or packages that are specifically geared for computer vision. Part III, "Programming For Computer Vision," describes two different approaches to facilitate programming for computer vision.