Bio-inspired model for robust motion detection under noisy conditions

Russell S.A. Brinkworth, David C. O'Carroll

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

The neuronal pathway for biological motion vision is complex and non-linear. Despite considerable research effort it has defied accurate modelling for over 50 years. Recently we proposed a computational model for the calculation of egomotion that explained a number of outstanding issues, such as reliable coding in different environments and responses to artificially contrast rescaled images. Here we varied the amount of noise to determine the robustness of the model under conditions more typical of real-world scenes. High-dynamic range panoramic images taken from various environments were used as inputs to a computational motion model of biological motion vision. Gaussian white noise was added after image pre-processing but before motion detection. The addition of noise around the levels observed experimentally, in both biology and an engineered camera system, resulted in a surprising 50% increase in the discriminability of different velocity levels over that seen in the noise free condition. The more commonly used gradient model for motion detection produced outputs so swamped by noise they were unreliable under the same conditions. While the phenomenon of stochastic resonance has been observed previously in biological and bio-inspired systems it is most commonly found in conjunction with non-linear thresholding operations, such as spike generation. These findings are unusual as they show noise being beneficial in a model of an analogue system. They also highlight the robustness of the correlation model for biological motion detection to very large levels of noise.

Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
CountrySpain
CityBarcelona
Period18/07/1023/07/10

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  • Cite this

    Brinkworth, R. S. A., & O'Carroll, D. C. (2010). Bio-inspired model for robust motion detection under noisy conditions. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 [5596502] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2010.5596502