Image processing is progressively becoming a science, but it remains closer to techniques, than to theory. Hence one needs to define a common frame to settle general enough problems and to build systems for various purposes in vision: a systematic method for tackling applications in computer vision.
The tentative method here stems from three main principles:
- taking the operational framework into account, to get constraints,
- introducing specific knowledge related to the application as early as possible,
- extracting local and global image properties at both segmentation and matching steps.
As it will be shown along the paper, all principles ask for an explicit expertise on classical image processing techniques: application bounds and limits. They lead to less classical image procedures, to be especially developped as in the present case:
- cooperative segmentation,
- use of planarity constraints.
Two applications have been selected, they are different on both their operational framework and image processing problems they pose :
- target tracking in IR imagery,
- 3D scene reconstruction of classical mobile robot environments: indoor or outdoor urban scenes.
Both systems have been actually designed and built. It is impossible to prove any generality of a method based on two different applications only. But these have been selected to be generic enough and with sufficient change between them, so that our systematic method of applicative system designing be likely used with succes in many other image processing applications.
After explaining the systematic method outlines through the three basic principles, each principle is illustrated by examples derived from both abovementionned applications.