{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T09:23:29Z","timestamp":1762161809096,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T00:00:00Z","timestamp":1702252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Association Nationale de la Recherche et de la Technologie"},{"name":"French National Research Agency (ANR)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Event-based clustering provides a low-power embedded solution for low-level feature extraction in a scene. The algorithm utilizes the non-uniform sampling capability of event-based image sensors to measure local intensity variations within a scene. Consequently, the clustering algorithm forms similar event groups while simultaneously estimating their attributes. This work proposes taking advantage of additional event information in order to provide new attributes for further processing. We elaborate on the estimation of the object velocity using the mean motion of the cluster. Next, we are examining a novel form of events, which includes intensity measurement of the color at the concerned pixel. These events may be processed to estimate the rough color of a cluster, or the color distribution in a cluster. Lastly, this paper presents some applications that utilize these features. The resulting algorithms are applied and exercised thanks to a custom event-based simulator, which generates videos of outdoor scenes. The velocity estimation methods provide satisfactory results with a trade-off between accuracy and convergence speed. Regarding color estimation, the luminance estimation is challenging in the test cases, while the chrominance is precisely estimated. The estimated quantities are adequate for accurately classifying objects into predefined categories.<\/jats:p>","DOI":"10.3390\/s23249768","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T14:12:51Z","timestamp":1702303971000},"page":"9768","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Velocity and Color Estimation Using Event-Based Clustering"],"prefix":"10.3390","volume":"23","author":[{"given":"Xavier","family":"Lesage","sequence":"first","affiliation":[{"name":"Univ. Grenoble Alpes, CNRS (National Centre for Scientific Research), Grenoble INP (Institute of Engineering), TIMA (Techniques of Informatics and Microelectronics for Integrated Systems Architecture), F-38000 Grenoble, France"},{"name":"Orioma, F-38430 Moirans, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rosalie","family":"Tran","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, CNRS (National Centre for Scientific Research), Grenoble INP (Institute of Engineering), TIMA (Techniques of Informatics and Microelectronics for Integrated Systems Architecture), F-38000 Grenoble, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"St\u00e9phane","family":"Mancini","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, CNRS (National Centre for Scientific Research), Grenoble INP (Institute of Engineering), TIMA (Techniques of Informatics and Microelectronics for Integrated Systems Architecture), F-38000 Grenoble, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6045-8510","authenticated-orcid":false,"given":"Laurent","family":"Fesquet","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, CNRS (National Centre for Scientific Research), Grenoble INP (Institute of Engineering), TIMA (Techniques of Informatics and Microelectronics for Integrated Systems Architecture), F-38000 Grenoble, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/TPAMI.2020.3008413","article-title":"Event-Based Vision: A Survey","volume":"44","author":"Gallego","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lesage, X., Tran, R., Mancini, S., and Fesquet, L. (2022, January 22\u201324). An improved event-by-event clustering algorithm for noisy acquisition. 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