Part III: Programming for Computer Vision

One of the more overlooked areas in computer vision is its programming aspect. Because of the heavy dependence of computer vision on image and range data, the proper programming environment has to efficiently handle and manipulate such data. It also has to contain the fundamental operations such as input/output and basic operations such as image convolution and 2D blob extraction. This allows the programmer to concentrate on prototyping the algorithm currently being researched.

There are two chapters in this final section that address the issue of programming for computer vision. In Chapter 11, Bradski describes the well-known Open Source Computer Vision Library (OpenCV). OpenCV is a collection of C and C++ source code and executables that are optimized for real-time vision applications. He shows how a variety of applications such as stereo, tracking, and face detection can be easily implemented using OpenCV.

In Chapter 12, François highlights and addresses architecture-level software development issues facing researchers and practitioners in the field of computer vision. A new framework, or architectural style, called SAI, is introduced. It provides a formalism for the design, implementation, and analysis of software systems that perform distributed parallel processing of generic data streams. Architectural patterns are illustrated with a number of demonstration projects ranging from single-stream, automatic, real-time video processing to fully integrated, distributed, interactive systems mixing live video, graphics, and sound. SAI is supported by an open-source architectural middleware called MFSM.