Vision for Cognitive Systems: A New Compound Concept Connecting Natural Scenes with Cognitive Models

Vision, as a key perceptional capability for cognitive systems relates to rather difficult problems -such as visual object recognition, representation, categorization, and scene understanding. State-of-the-art solutions, using object appearance based models, already reached certain maturity. They achieve excellent recognition performance and provide learning structures that are subsequently utilized for object recognition and tracking. However, in context of object topology understanding for cognitive tasks, these methods cannot be directly compared with human performance, because it is obvious that appearance based methods do not contribute to understanding of structures in 3D. Research findings from infant psychology and animal investigation give evidence for using hierarchical models of object representation, based on image primitives e.g. such as edges, corners, shading or homogeneity of object colors. It is the objective of this paper to present an approach based on both, findings from biological studies and cognitive science, as enablers for autonomous cognitive investigation of natural scenes and their understanding. We present the architecture of a compound cognitive framework and its first behavioral level with the implementation of a vision model of the mammalian striate visual cortex in five layers. The proposed implementation is exemplified with an object similar to the Necker cube.