Modular design of the segmentation unit of hierarchical computer vision systems

This paper describes a systematic approach to the design of the segmentation or lower-level processing unit of a general-purpose computer vision system. It is developed within the context of the human visual system and mathematical pattern recognition theory. The eventual goal of the research is to integrate these two concepts to obtain the visually distinct segments of an image, which have vital importance for the interpretation or higher-level processing unit of a vision system. A new computational (pattern recognition) technique is proposed in accordance with the human color perception. It operates in unsupervised mode without using any training prototypes or requiring a priori knowledge about the input scenes. However, it can also be implemented in supervised mode by means of including external knowledge. In order to obtain a set of features most useful for a particular image, a new feature extraction method is devised in the form of a reference feature library. It is a statistical-structural technique which combines the spatial and spectral information in the local image areas.