The resonant retina: Exploiting vibration noise to optimally detect edges in an image

Hongler, Max-Olivier; Meneses, Yuri López de; Beyeler, Antoine; Jacot, Jacques

doi:10.1109/tpami.2003.1227982

Cited by 77 publications

(55 citation statements)

References 22 publications

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“…The fundamental characteristic of this interesting phenomenon has been discussed in the context of nonlinear physics [2,3], and the study of SR has spread to field such as neural systems [4,5], human machine systems [6], nanotechnology [7], image processing [8,9], electrical circuits [10,11], and wireless communication.…”

Section: Introductionmentioning

confidence: 99%

Effect of a root-raised-cosine filter on a BPSK stochastic resonance receiver

Nakashima

Tanaka

Yamazato

et al. 2017

NOLTA

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Signal filtering is necessary for wireless communication. However it causes the signal amplitude to fluctuate and affects the performance of stochastic resonance (SR) receivers. In this study, we evaluate the bit error rate (BER) performance of filtered binary phase-shift keying (BPSK) on an SR receiver. The results show that filtering improves the BER performance of the SR receiver because the amplitude fluctuation contributes to improving the SR effect. We also evaluate the effect of the roll-off factor, which determines the bandwidth of the filter and the amplitude fluctuation. The results demonstrate the applicability of the SR receiver to bandlimited BPSK signals.

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Section: Introductionmentioning

confidence: 99%

Effect of a root-raised-cosine filter on a BPSK stochastic resonance receiver

Nakashima

Tanaka

Yamazato

et al. 2017

NOLTA

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“…The first work on stochastic resonance was reported in [1]. Recently some of the applications based on stochastic resonance for grayscale image or edge enhancement have been reported in literatures [2][3][4][5][6][7][8]. The presence of darkness due to insufficient illumination, image enhancement is required for better visualization of dark images that are having low dynamic range intensity values.…”

Section: Introductionmentioning

confidence: 99%

Undecimated Balanced GHM Multiwavelet Transform based Contrast Enhancement Technique for Dark Images using Dynamic Stochastic Resonance

Deivalakshmi¹,

Palanisamy²

2016

IJCA

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“…pigeons bob its head forth and back to obtain depth perception, since the pigeon eyes in the head side so the motion is in the lateral direction for pigeons [15]. Camera oscillation had been used for edge detection, contrast detection and motion analysis [10,17].…”

Section: Introductionmentioning

confidence: 99%

Dynamic feature detection using virtual correction and camera oscillations

Heshmat

Abdellatif

Nakamura

et al. 2014

2014 International Conference on 3D Imaging (IC3D)

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Visual SLAM algorithms exploit natural scene features to infer the camera motion and build a map of a static environment. In this paper, we relax the severe assumption of a static scene to allow for the detection and deletion of dynamic points. A new "virtual correction" method is introduced which serves to detect the dynamic points by checking the re-projection error of the points before and after the virtual measurement update. It can also recover the erroneously excluded useful features, particularly the distant points which may be deleted because of the change in its position after new measurement observation. Deliberate camera oscillations are also used to improve the VSLAM accuracy and the camera observability. The simulation results showed the effectiveness of the virtual correction in improving the consistency of the VSLAM and the detection of dynamic points and in particular for difficult scenarios.

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The resonant retina: Exploiting vibration noise to optimally detect edges in an image

Cited by 77 publications

References 22 publications

Effect of a root-raised-cosine filter on a BPSK stochastic resonance receiver

Effect of a root-raised-cosine filter on a BPSK stochastic resonance receiver

Undecimated Balanced GHM Multiwavelet Transform based Contrast Enhancement Technique for Dark Images using Dynamic Stochastic Resonance

Dynamic feature detection using virtual correction and camera oscillations

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