Cost and information-driven algorithm selection for vision systems

A computer vision application can be defined as a sequence of image processing, feature extraction, and interpretation operations that are used to solve a specific task. In most of the computer vision systems this sequence is pre-defined and static. The work presented here shows a dynamic technique for algorithm selection based on both the value of information of each operation, and the computational complexity of the operations, which is modeled in terms of cost. This technique is used in the Ascender II system to select visual operators to perform aerial image interpretation of urban regions. The results show that the cost and information driven method presented here leads to performance gains. Moreover the hierarchical structure of the system simplifies the addition of new visual operations.