Cognitive Architectures for Sensory Processing

Abstract

This paper describes our efforts to design a cognitive architecture for object recognition in video. Unlike most efforts in computer vision, our work proposes a Bayesian approach to object recognition in video, using a hierarchical, distributed architecture of dynamic processing elements that learns in a self-organizing way to cluster objects in the video input. A biologically inspired innovation is to implement a top-down pathway across layers in the form of causes, creating effectively a bidirectional processing architecture with feedback. To simplify discrimination, overcomplete representations are utilized. Both inference and parameter learning are performed using empirical priors, while imposing appropriate sparseness constraints. Preliminary results show that the cognitive architecture has features that resemble the functional organization of the early visual cortex. One example showing the use of top-down connections is given to disambiguate a synthetic video from correlated noise.