Abstract

F. Miau, C. Papageorgiou, L. Itti, Neuromorphic algorithms for computer vision and attention, In: Proc. SPIE 46 Annual International Symposium on Optical Science and Technology, (B. Bosacchi, D. B. Fogel, J. C. Bezdek Ed.), Vol. 4479, pp. 12-23, Bellingham, WA:SPIE Press, Nov 2001. (Cited by 68)

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.

Keywords: Visual attention ; object recognition ; scene analysis ; bottom-up ; top-down

Themes: Model of Bottom-Up Saliency-Based Visual Attention, Computational Modeling, Computer Vision