Research Report

Cognitive Processing

, Volume 15, Issue 1, pp 13-28

First online:

A biologically based model for recognition of 2-D occluded patterns

  • Mohammad SaifullahAffiliated withDepartment of Computer and Information Science, Linköping University Email author 
  • , Christian BalkeniusAffiliated withLund University Cognitive Science
  • , Arne JönssonAffiliated withDepartment of Computer and Information Science, Linköping University

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In this work, we present a biologically inspired model for recognition of occluded patterns. The general architecture of the model is based on the two visual information processing pathways of the human visual system, i.e. the ventral and the dorsal pathways. The proposed hierarchically structured model consists of three parallel processing channels. The main channel learns invariant representations of the input patterns and is responsible for pattern recognition task. But, it is limited to process one pattern at a time. The direct channel represents the biologically based direct connection from the lower to the higher processing level in the human visual cortex. It computes rapid top-down pattern-specific cues to modulate processing in the other two channels. The spatial channel mimics the dorsal pathway of the visual cortex. It generates a combined saliency map of the input patterns and, later, segments the part of the map representing the occluded pattern. This segmentation process is based on our hypothesis that the dorsal pathway, in addition to encoding spatial properties, encodes the shape representations of the patterns as well. The lateral interaction between the main and the spatial channels at appropriate processing levels and top-down, pattern-specific modulation of the these two channels by the direct channel strengthen the locations and features representing the occluded pattern. Consequently, occluded patterns become focus of attention in the ventral channel and also the pattern selected for further processing along this channel for final recognition.


Vision Occluded patterns Neural network model Interactive process Attention Segmentation and recognition