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Comput. Eng."],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Neuromorphic vision has made significant progress in recent years, thanks to the natural match between spiking neural networks and event data in terms of biological inspiration, energy savings, latency and memory use for dynamic visual data processing. However, optimising its energy requirements still remains a challenge within the community, especially for embedded applications. One solution may reside in preprocessing events to optimise data quantities thus lowering the energy cost of neuromorphic hardware, proportional to the number of synaptic operations. To this end, we extend an end-to-end neuromorphic line detection mechanism to introduce line-based event data preprocessing. Our results demonstrate on three benchmark event-based datasets that preprocessing leads to an advantageous trade-off between energy consumption and classification performance. Depending on the line-based preprocessing strategy and the complexity of the classification task, we show that one can maintain or increase the classification accuracy while significantly reducing the theoretical energy consumption. Our approach systematically leads to a significant improvement of the neuromorphic classification efficiency, thus laying the groundwork towards a more frugal neuromorphic computer vision thanks to event preprocessing.<\/jats:p>","DOI":"10.1088\/2634-4386\/ae5128","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:49:41Z","timestamp":1773355781000},"page":"024002","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Line-based event preprocessing: towards low-energy neuromorphic computer vision"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3916-0514","authenticated-orcid":true,"given":"Am\u00e9lie","family":"Gruel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8311-8526","authenticated-orcid":false,"given":"Pierre","family":"Lewden","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9770-4236","authenticated-orcid":false,"given":"Adrien F","family":"Vincent","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1414-2523","authenticated-orcid":false,"given":"Sylvain","family":"Sa\u00efghi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"nceae5128bib1","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1016\/S0893-6080(97)00011-7","type":"journal-article","article-title":"Networks of spiking neurons: the third generation of neural network models","volume":"10","author":"Maass","year":"1997","journal-title":"Neural Netw."},{"key":"nceae5128bib2","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1109\/JSSC.2007.914337","type":"journal-article","article-title":"A 128x128 120 dB 15 us latency asynchronous temporal contrast vision sensor","volume":"43","author":"Lichtsteiner","year":"2008","journal-title":"IEEE J. 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