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On event-based optical flow detection

Event-based sensing, i.e., the asynchronous detection of luminance changes, promises low-energy, high dynamic range, and sparse sensing. This stands in contrast to whole image frame-wise acquisition by standard cameras. Here, we systematically investigate the implications of event-based sensing in t...

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Detalles Bibliográficos
Autores principales: Brosch, Tobias, Tschechne, Stephan, Neumann, Heiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4403305/
https://www.ncbi.nlm.nih.gov/pubmed/25941470
http://dx.doi.org/10.3389/fnins.2015.00137
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author Brosch, Tobias
Tschechne, Stephan
Neumann, Heiko
author_facet Brosch, Tobias
Tschechne, Stephan
Neumann, Heiko
author_sort Brosch, Tobias
collection PubMed
description Event-based sensing, i.e., the asynchronous detection of luminance changes, promises low-energy, high dynamic range, and sparse sensing. This stands in contrast to whole image frame-wise acquisition by standard cameras. Here, we systematically investigate the implications of event-based sensing in the context of visual motion, or flow, estimation. Starting from a common theoretical foundation, we discuss different principal approaches for optical flow detection ranging from gradient-based methods over plane-fitting to filter based methods and identify strengths and weaknesses of each class. Gradient-based methods for local motion integration are shown to suffer from the sparse encoding in address-event representations (AER). Approaches exploiting the local plane like structure of the event cloud, on the other hand, are shown to be well suited. Within this class, filter based approaches are shown to define a proper detection scheme which can also deal with the problem of representing multiple motions at a single location (motion transparency). A novel biologically inspired efficient motion detector is proposed, analyzed and experimentally validated. Furthermore, a stage of surround normalization is incorporated. Together with the filtering this defines a canonical circuit for motion feature detection. The theoretical analysis shows that such an integrated circuit reduces motion ambiguity in addition to decorrelating the representation of motion related activations.
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spelling pubmed-44033052015-05-04 On event-based optical flow detection Brosch, Tobias Tschechne, Stephan Neumann, Heiko Front Neurosci Neuroscience Event-based sensing, i.e., the asynchronous detection of luminance changes, promises low-energy, high dynamic range, and sparse sensing. This stands in contrast to whole image frame-wise acquisition by standard cameras. Here, we systematically investigate the implications of event-based sensing in the context of visual motion, or flow, estimation. Starting from a common theoretical foundation, we discuss different principal approaches for optical flow detection ranging from gradient-based methods over plane-fitting to filter based methods and identify strengths and weaknesses of each class. Gradient-based methods for local motion integration are shown to suffer from the sparse encoding in address-event representations (AER). Approaches exploiting the local plane like structure of the event cloud, on the other hand, are shown to be well suited. Within this class, filter based approaches are shown to define a proper detection scheme which can also deal with the problem of representing multiple motions at a single location (motion transparency). A novel biologically inspired efficient motion detector is proposed, analyzed and experimentally validated. Furthermore, a stage of surround normalization is incorporated. Together with the filtering this defines a canonical circuit for motion feature detection. The theoretical analysis shows that such an integrated circuit reduces motion ambiguity in addition to decorrelating the representation of motion related activations. Frontiers Media S.A. 2015-04-20 /pmc/articles/PMC4403305/ /pubmed/25941470 http://dx.doi.org/10.3389/fnins.2015.00137 Text en Copyright © 2015 Brosch, Tschechne and Neumann. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Brosch, Tobias
Tschechne, Stephan
Neumann, Heiko
On event-based optical flow detection
title On event-based optical flow detection
title_full On event-based optical flow detection
title_fullStr On event-based optical flow detection
title_full_unstemmed On event-based optical flow detection
title_short On event-based optical flow detection
title_sort on event-based optical flow detection
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4403305/
https://www.ncbi.nlm.nih.gov/pubmed/25941470
http://dx.doi.org/10.3389/fnins.2015.00137
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