Event-based clustering and looming detection

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dc.contributor.authorKamranian, Behnam
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorCheng, Howard
dc.date.accessioned2019-11-29T17:44:56Z
dc.date.available2019-11-29T17:44:56Z
dc.date.issued2019
dc.degree.levelMastersen_US
dc.description.abstractBased on the sequential K-means algorithm, we present a real-time, accurate and automatic clustering method for asynchronous events generated by the optical flow algorithm of Ridwan and Cheng. The complexity of our algorithm does not increase with increasing number of events. We also designed an implementation of the elbow method capable of detecting the number of clusters without any a priori assumptions on objects. In addition, we designed a merge algorithm capable of merging multiple touching clusters into one for enhancing the results of our clustering algorithm. The output of our clustering algorithm is then used with a single object looming detection algorithm to detect looming for multiple objects. We tested our algorithm on both simulated and captured data sets against two other well-known algorithms. Our algorithm is fast and accurate both in cluster detection quality and looming detection quality.en_US
dc.identifier.urihttps://hdl.handle.net/10133/5596
dc.language.isoen_USen_US
dc.proquest.subjectComputer science [0984]en_US
dc.proquest.subjectRobotics [0771]en_US
dc.proquestyesYesen_US
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Scienceen_US
dc.publisher.departmentDepartment of Mathematics and Computer Scienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectComputer visionen_US
dc.subjectComputer algorithmsen_US
dc.subjectDigital images Deconvolutionen_US
dc.subjectOpticsen_US
dc.titleEvent-based clustering and looming detectionen_US
dc.typeThesisen_US
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