Bottleneck Problem Solution using Biological Models of Attention in High Resolution Tracking Sensors

Every high resolution imaging system suffers from the bottleneck problem. This problem relates to the huge amount of data transmission from the sensor array to a digital signal processing (DSP) and to bottleneck in performance, caused by the requirement to process a large amount of information in parallel. The same problem exists in biological vision systems, where the information, sensed by many millions of receptors should be transmitted and processed in real time. Models, describing the bottleneck problem solutions in biological systems fall in the field of visual attention. This paper presents the bottleneck problem existing in imagers used for real time salient target tracking and proposes a simple solution by employing models of attention, found in biological systems. The bottleneck problem in imaging systems is presented, the existing models of visual attention are discussed and the architecture of the proposed imager is shown.

Towards Active Vision in the DUAL Cognitive Architecture

The paper describes an extension of the cognitive architecture DUAL with a model of visual attention and perception. The goal of this attempt is to account for the construction and the categorization of object and scene representations derived from visual stimuli in the TextWorld microdomain. Low-level parallel computations are combined with an active serial deployment of visual attention enabling the construction of abstract symbolic representations. A limited-capacity short-term visual store holding information across attention shifts forms the core of the model interfacing between the low-level representation of the stimulus and DUAL’s semantic memory. The model is validated by comparing the results of a simulation with real data from an eye movement experiment with human subjects.