Neuromorphic algorithms for computer vision and attention

Florence Miau; Constantine S. Papageorgiou; Laurent Itti

doi:10.1117/12.448343

14 November 2001 Neuromorphic algorithms for computer vision and attention

Florence Miau, Constantine S. Papageorgiou, Laurent Itti

Proceedings Volume 4479, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation IV; (2001) https://doi.org/10.1117/12.448343
Event: International Symposium on Optical Science and Technology, 2001, San Diego, CA, United States

Abstract

We describe an integrated vision system which reliably detects persons in static color natural scenes, or other targets among distracting objects. The system is built upon the biologically-inspired synergy between two processing stages: A fast trainable visual attention front-end (where), which rapidly selects a restricted number of conspicuous image locations, and a computationally expensive object recognition back-end (what), which determines whether the selected locations are targets of interest. We experiment with two recognition back-ends: One uses a support vector machine algorithm and achieves highly reliable recognition of pedestrians in natural scenes, but is not particularly biologically plausible, while the other is directly inspired from the neurobiology of inferotemporal cortex, but is not yet as robust with natural images. Integrating the attention and recognition algorithms yields substantial speedup over exhaustive search, while preserving detection rate. The success of this approach demonstrates that using a biological attention-based strategy to guide an object recognition system may represent an efficient strategy for rapid scene analysis.

Citation Download Citation

Florence Miau, Constantine S. Papageorgiou, and Laurent Itti "Neuromorphic algorithms for computer vision and attention", Proc. SPIE 4479, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation IV, (14 November 2001); https://doi.org/10.1117/12.448343

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