An insect vision-based motion detection chip

Moini, Alireza; Bouzerdoum, A.; Eshraghian, Kamran; Yakovleff, A.; Nguyen, X.T.; Blanksby, Andrew; Beare, Richard; Abbott, Derek; Bogner, R.E.

doi:10.1109/4.551924

Cited by 85 publications

(41 citation statements)

References 13 publications

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“…By detecting changes in chrominance as well as changes in intensity, more accurate templates can be found, and thus a more reliable estimation of the motion can be made [10]. The Template Model can easily be implemented in hardware also, and multiple generations of insect vision chips based on this model have been developed by Moini et al [11,12,13,14,15,16,17]. The software implementation of the Template Model is illustrated below: So consecutive frames, containing luminance or chrominance information, are captured (e.g.…”

Section: Template Modelmentioning

confidence: 99%

Implementation of insect-vision-based motion detection models using a video camera

Budimir

Correll

Shrivastava

et al. 2005

Smart Structures, Devices, and Systems II

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Despite their limited information processing capabilities, insects (with brains smaller than a pinhead) are able to manoeuvre with precision through environments that are highly-crowded and contain moving objects. Their ability to avoid collisions using limited computing power forms the basis for this project, in which we attempt to simulate the motion detection ability of insects using two models -the Horridge Template Model and the Reichardt Correlation Model. In this project, the direction of motion of a moving object and its angular speed are determined by capturing visual data using a web camera focussed on a moving pattern generated by VisionEgg software. The performance of both the models is quantitatively compared and various error-reducing techniques are investigated.

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Section: Template Modelmentioning

confidence: 99%

Implementation of insect-vision-based motion detection models using a video camera

Budimir

Correll

Shrivastava

et al. 2005

Smart Structures, Devices, and Systems II

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“…In 1997, Moini et al published a paper describing the design and architecture of a VLSI motion detection chip [32]. This chip is based on the Horridge template model of the insect vision system [33] and is based on a (nonlinear) neuronal model.…”

Section: Sr In Insect Vision Based Motion Detectionmentioning

confidence: 99%

“…In the VLSI implementations of the template model designed by Moini et al [32], at each receiver the input signal is time differentiated and then thresholded. Note that since we require a case of no change, there are in fact two thresholds required.…”

Section: Sr In the Template Modelmentioning

confidence: 99%

Open Questions For Suprathreshold Stochastic Resonance In Sensory Neural Models for Motion Detection Using Artificial Insect Vision

McDonnell¹,

Abbott²

2003

AIP Conference Proceedings

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Abstract. Stochastic Resonance (SR) occurs when the presence of noise in a nonlinear system can induce an optimal output from that system, and has been observed in a diverse range of physical and biological systems, including neurons. Despite this widespread observation of SR, to date very few engineering applications inspired by SR have been proposed, and one of the goals of our research is to explore possible new practical applications designed to replicate the benefits of SR. In particular, since about 1991, our group has designed and implemented a number of motion detection VLSI chips based on insect vision. We are currently investigating the possibility of replicating the benefits of SR in artificial insect-vision based motion detection systems, in particular a newly described form of SR called Suprathreshold Stochastic Resonance (SSR). The current paper is intended to review and identify the key open questions and avenues for future research relating to SR and SSR in such systems.

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“…Neuromorphic motion sensors based on different motion algorithms, for example, correlation-based motion, time-to-travel, energy models, and optical flow [1][2][3][4][5][6][7][8][9][10], are being incorporated as an on-flight or robotic sensor to replace CCD cameras and digital processors. The characteristics of the sensors; low-power consumption (W), light-weight (several grams), analog collective computation, and robustness to different light and contrast conditions [11], make them attractive for mobile, battery powered robots [12].…”

Section: Motivationmentioning

confidence: 99%