A Bio-Inspired Neuromorphic Active Vision System Based on Fixational Eye Movements

Testa, Simone; Indiveri, Giacomo; Sabatini, Silvio P.

doi:10.1109/iscas45731.2020.9180439

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…We expect that the activity elicited in each receptor of our artificial retina complies with the statistical properties of natural images in the same way it happens in biology. In case of stimuli with a fixed contrast value, results showed that pixels’ mean activity grows as spatial frequency increases (see 15 and Supplementary Fig.S1 online). As a matter of fact, by randomly moving around, each receptor scans an increasing number of edges as the spatial frequency grows, thus eliciting an increasing number of events.…”

Section: Resultsmentioning

confidence: 98%

Section: Decorrelation Of Natural Imagesmentioning

confidence: 99%

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Active Fixation as an Efficient Coding Strategy for Neuromorphic Vision

Testa

Sabatini

Canessa

2022

Preprint

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Contrary to a photographer, who puts a great effort in keeping the lens still, eyes insistently move even during fixation. This benefits signal decorrelation, which underlies an efficient encoding of visual information. Yet, camera motion is not sufficient alone; it must be coupled with a sensor specifically selective to temporal changes. Indeed, motion induced on standard imagers only results in burring effects. Neuromorphic sensors represent a valuable solution. Here we characterize the response of an event-based camera equipped with Fixational Eye Movements (FEMs) on both synthetic and natural images. Our analyses prove that the system starts an early stage of redundancy suppression, as a precursor of subsequent whitening processes on the amplitude spectrum. This does not come at the price of corrupting structural information contained in local spatial phase across oriented axes. Isotropy of FEMs ensures proper representations of image features without introducing biases towards specific contrast orientations.

show abstract

Section: Resultsmentioning

confidence: 98%

Section: Decorrelation Of Natural Imagesmentioning

confidence: 99%

Active Fixation as an Efficient Coding Strategy for Neuromorphic Vision

Testa

Sabatini

Canessa

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Active fixation as an efficient coding strategy for neuromorphic vision

Testa

Sabatini

Canessa

2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

Contrary to a photographer, who puts a great effort in keeping the lens still, eyes insistently move even during fixation. This benefits signal decorrelation, which underlies an efficient encoding of visual information. Yet, camera motion is not sufficient alone; it must be coupled with a sensor specifically selective to temporal changes. Indeed, motion induced on standard imagers only results in burring effects. Neuromorphic sensors represent a valuable solution. Here we characterize the response of an event-based camera equipped with fixational eye movements (FEMs) on both synthetic and natural images. Our analyses prove that the system starts an early stage of redundancy suppression, as a precursor of subsequent whitening processes on the amplitude spectrum. This does not come at the price of corrupting structural information contained in local spatial phase across oriented axes. Isotropy of FEMs ensures proper representations of image features without introducing biases towards specific contrast orientations.

show abstract

A Bio-Inspired Neuromorphic Active Vision System Based on Fixational Eye Movements

Cited by 2 publications

References 18 publications

Active Fixation as an Efficient Coding Strategy for Neuromorphic Vision

Active Fixation as an Efficient Coding Strategy for Neuromorphic Vision

Active fixation as an efficient coding strategy for neuromorphic vision

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