Despite that, the image understanding process remains an open area of computer vision and is under continued investigation. Image understanding is one of the most complex challenges of AI, and to cover this complicated area of computer vision in detail it would be necessary to discuss relatively independent branches of AI knowledge representation relational structures semantic networks general matching inference production systems problem solving planning control feedback learning from experience etc. Image understanding can be achieved only as a result of cooperation of complex information processing tasks and appropriate control of these tasks. Biological systems include a very complicated and complex control strategy incorporating parallel processing, dynamic sensing subsystem allocation, behavior modifications, interrupt driven shifts of attention, etc. As in other AI problems, the main goal of computer vision is to achieve machine behavior similar to that of biological systems by applying technically available procedures .
Operations are always sequential in serial processing.
Almost all low-level image processing can be done in parallel. High-level processing using higher levels of abstraction is usually serial in essence. Processing proceeds from the raster image to segmented image, to region description, and to their recognition. A set of assumptions and expected properties is constructed from applicable knowledge. The image understanding is an internal model verification, and the model is either accepted or rejected.