Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/90285
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Type: Journal article
Title: Biologically inspired feature detection using cascaded correlations of off and on channels
Author: Wiederman, S.
O’Carroll, D.
Citation: Journal of Artificial Intelligence and Soft Computing Research, 2013; 3(1):5-14
Publisher: De Gruyter
Issue Date: 2013
ISSN: 2083-2567
2083-2567
Statement of
Responsibility: 
Steven D. Wiederman and David C. O'Carroll
Abstract: Flying insects are valuable animal models for elucidating computational processes underlying visual motion detection. For example, optical flow analysis by wide-field motion processing neurons in the insect visual system has been investigated from both behavioral and physiological perspectives [1]. This has resulted in useful computational models with diverse applications [2,3]. In addition, some insects must also extract the movement of their prey or conspecifics from their environment. Such insects have the ability to detect and interact with small moving targets, even amidst a swarm of others [4,5]. We use electrophysiological techniques to record from small target motion detector (STMD) neurons in the insect brain that are likely to subserve these behaviors. Inspired by such recordings, we previously proposed an ‘elementary’ small target motion detector (ESTMD) model that accounts for the spatial and temporal tuning of such neurons and even their ability to discriminate targets against cluttered surrounds [6-8]. However, other properties such as direction selectivity [9] and response facilitation for objects moving on extended trajectories [10] are not accounted for by this model. We therefore propose here two model variants that cascade an ESTMD model with a traditional motion detection model algorithm, the Hassenstein Reichardt ‘elementary motion detector’ (EMD) [11]. We show that these elaborations maintain the principal attributes of ESTMDs (i.e. spatiotemporal tuning and background clutter rejection) while also capturing the direction selectivity observed in some STMD neurons. By encapsulating the properties of biological STMD neurons we aim to develop computational models that can simulate the remarkable capabilities of insects in target discrimination and pursuit for applications in robotics and artificial vision systems.
Rights: © 2015. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)
RMID: 0030024989
DOI: 10.2478/jaiscr-2014-0001
Grant ID: http://purl.org/au-research/grants/arc/DP130104572
Appears in Collections:Medical Sciences publications

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