Translator Disclaimer
30 April 2007 Bio-inspired visual attention and object recognition
Author Affiliations +
This paper describes a bio-inspired Visual Attention and Object Recognition System (VARS) that can (1) learn representations of objects that are invariant to scale, position and orientation; and (2) recognize and locate these objects in static and video imagery. The system uses modularized bio-inspired algorithms/techniques that can be applied towards finding salient objects in a scene, recognizing those objects, and prompting the user for additional information to facilitate interactive learning. These algorithms are based on models of human visual attention, search, recognition and learning. The implementation is highly modular, and the modules can be used as a complete system or independently. The underlying technologies were carefully researched in order to ensure they were robust, fast, and could be integrated into an interactive system. We evaluated our system's capabilities on the Caltech-101 and COIL-100 datasets, which are commonly used in machine vision, as well as on simulated scenes. Preliminary results are quite promising in that our system is able to process these datasets with good accuracy and low computational times.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Deepak Khosla, Christopher K. Moore, David Huber, and Suhas Chelian "Bio-inspired visual attention and object recognition", Proc. SPIE 6560, Intelligent Computing: Theory and Applications V, 656003 (30 April 2007);

Back to Top