Small-scale anomaly detection in panoramic imaging using neural models of low-level vision

Matthew C. Casey; Duncan L. Hickman; Athanasios Pavlou; James R. E. Sadler

doi:10.1117/12.883799

7 June 2011 Small-scale anomaly detection in panoramic imaging using neural models of low-level vision

Matthew C. Casey, Duncan L. Hickman, Athanasios Pavlou, James R. E. Sadler

Proceedings Volume 8042, Display Technologies and Applications for Defense, Security, and Avionics V; and Enhanced and Synthetic Vision 2011; 80420X (2011) https://doi.org/10.1117/12.883799
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Our understanding of sensory processing in animals has reached the stage where we can exploit neurobiological principles in commercial systems. In human vision, one brain structure that offers insight into how we might detect anomalies in real-time imaging is the superior colliculus (SC). The SC is a small structure that rapidly orients our eyes to a movement, sound or touch that it detects, even when the stimulus may be on a small-scale; think of a camouflaged movement or the rustle of leaves. This automatic orientation allows us to prioritize the use of our eyes to raise awareness of a potential threat, such as a predator approaching stealthily. In this paper we describe the application of a neural network model of the SC to the detection of anomalies in panoramic imaging. The neural approach consists of a mosaic of topographic maps that are each trained using competitive Hebbian learning to rapidly detect image features of a pre-defined shape and scale. What makes this approach interesting is the ability of the competition between neurons to automatically filter noise, yet with the capability of generalizing the desired shape and scale. We will present the results of this technique applied to the real-time detection of obscured targets in visible-band panoramic CCTV images. Using background subtraction to highlight potential movement, the technique is able to correctly identify targets which span as little as 3 pixels wide while filtering small-scale noise.

Citation Download Citation

Matthew C. Casey, Duncan L. Hickman, Athanasios Pavlou, and James R. E. Sadler "Small-scale anomaly detection in panoramic imaging using neural models of low-level vision", Proc. SPIE 8042, Display Technologies and Applications for Defense, Security, and Avionics V; and Enhanced and Synthetic Vision 2011, 80420X (7 June 2011); https://doi.org/10.1117/12.883799

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