Cooperative and asynchronous stereo vision for dynamic vision sensors
Dynamic vision sensors (DVSs) encode visual input as a stream of events generated upon relative light intensity changes in the scene. These sensors have the advantage of allowing simultaneously high temporal resolution (better than 10 µs) and wide dynamic range (>120 dB) at sparse data representa...
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Published in | Measurement science & technology Vol. 25; no. 5; pp. 55108 - 55115 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
IOP Publishing
01.05.2014
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Abstract | Dynamic vision sensors (DVSs) encode visual input as a stream of events generated upon relative light intensity changes in the scene. These sensors have the advantage of allowing simultaneously high temporal resolution (better than 10 µs) and wide dynamic range (>120 dB) at sparse data representation, which is not possible with clocked vision sensors. In this paper, we focus on the task of stereo reconstruction. The spatiotemporal and asynchronous aspects of data provided by the sensor impose a different stereo reconstruction approach from the one applied for synchronous frame-based cameras. We propose to model the event-driven stereo matching by a cooperative network (Marr and Poggio 1976 Science 194 283-7). The history of the recent activity in the scene is stored in the network, which serves as spatiotemporal context used in disparity calculation for each incoming event. The network constantly evolves in time, as events are generated. In our work, not only the spatiotemporal aspect of the data is preserved but also the matching is performed asynchronously. The results of the experiments prove that the proposed approach is well adapted for DVS data and can be successfully used for disparity calculation. |
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AbstractList | Dynamic vision sensors (DVSs) encode visual input as a stream of events generated upon relative light intensity changes in the scene. These sensors have the advantage of allowing simultaneously high temporal resolution (better than 10 µs) and wide dynamic range (>120 dB) at sparse data representation, which is not possible with clocked vision sensors. In this paper, we focus on the task of stereo reconstruction. The spatiotemporal and asynchronous aspects of data provided by the sensor impose a different stereo reconstruction approach from the one applied for synchronous frame-based cameras. We propose to model the event-driven stereo matching by a cooperative network (Marr and Poggio 1976 Science 194 283-7). The history of the recent activity in the scene is stored in the network, which serves as spatiotemporal context used in disparity calculation for each incoming event. The network constantly evolves in time, as events are generated. In our work, not only the spatiotemporal aspect of the data is preserved but also the matching is performed asynchronously. The results of the experiments prove that the proposed approach is well adapted for DVS data and can be successfully used for disparity calculation. |
Author | Belbachir, A N Piatkowska, E Gelautz, M |
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CitedBy_id | crossref_primary_10_1109_LRA_2018_2800793 crossref_primary_10_3389_fncir_2021_610446 crossref_primary_10_1109_TRO_2021_3062252 crossref_primary_10_1109_TPAMI_2020_3008413 crossref_primary_10_1002_aisy_202200221 crossref_primary_10_1145_3656469 |
Cites_doi | 10.1109/TNNLS.2011.2180025 10.1016/j.compind.2013.04.005 10.1016/j.compind.2013.02.003 10.1126/science.968482 10.1016/j.neunet.2013.03.006 10.1007/978-1-4613-1639-8 10.1109/34.865184 |
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References | 11 12 Bazargani H (13) 2012 Piatkowska E (14) 2013 Schraml S ed Belbachir A N (3) 2010 Kogler J (5) 2011 1 Delbruck T (2) 2008 Hess P (7) 2006 Schraml S (4) 2010 6 8 9 10 |
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SubjectTerms | asynchronous stereo dynamic vision sensors event-based processing |
Title | Cooperative and asynchronous stereo vision for dynamic vision sensors |
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