A Biologically-Inspired Model for Recognition of Overlapped Patterns

In this paper a biologically-inspired model for recognition of overlapped patterns is proposed. Information processing in the two visual information processing pathways, i.e., the dorsal and the ventral pathway, is modeled as a solution to the problem. We hypothesize that dorsal pathway, in addition to encoding the spatial information, learns the shape representation of the patterns and, later uses this knowledge as a top-down guidance signal to segment the bottom-up, image-based saliency map. This process of segmentation in the dorsal pathway is implemented as an interactive process, where interaction between bottom-up image information and top-down shape cues lead to incremental development of a segmented saliency map for one of the overlapped object at a time. This segmented map encodes spatial as well as shape information of the respective pattern in the input. The interaction of the dorsal channel with the ventral channel leads to modulation and selective processing of the respective pattern in the ventral pathway for final recognition. Simulation results support the presented hypothesis as well as effectiveness of the model in providing a solution to the recognition of overlapped patterns. The behavior of the model is in accordance to the known human behavior on the