The Imalab Method for Vision Systems
We propose a method to construct computer vision systems using a workbench composed of a multi-faceted toolbox and a general purpose kernel. The toolbox is composed of an open set of library modules. The kernel facilitates incremental dynamic system construction. This method makes it possible to quickly develop and experiment new algorithms, it simplifies the reuse of existing program libraries, and allows to construct a variety of systems to meet particular requirements. Major strong points of our approach are: (1) Imalab is a homogeneous environment for different types of users, who share the same basic code with different interfaces and tools. (2) Integration facility: modules for various scientific domains, in particular robotics or AI research (e.g. Bayesian reasoning, symbolic learning) can be integrated automatically. (3) Multilanguage integration: the C/C++ language and several symbolic programming languages - Lisp(Scheme), Prolog, Clips - are completely integrated. We consider this an important advantage for the implementation of cognitive vision functionalities. (4) Automatic program generation, to make multi-language integration work smoothly. (5) Efficiency: library code runs without overhead.
The Imalab system is in use for several years now, and we have started to distribute it.
KeywordsVision System Source Code Image Class Garbage Collector Interactive Shell
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