We are looking for applicants in Artificial Intelligence, Computer Vision, Edge Computing, Digital Twins, Human Computer Interaction, User Modelling, Robotics or Resilient Computing with potentials/achievements in informing space applications.
The post hoder will enjoy 1) a permanent (equivalent to US tenured) position at a top 100 university, 2) significantly reduced teaching, 3) a fully-funded PhD, 4) travel budget, 5) chance for a 2-year fully-funded Post-Doc.
Finding repetitive patterns is important to many applications such as bioinformatics, finance and speech processing, etc. Repetitive patterns can be either cyclic or acyclic such that the patterns are continuous and distributed respectively. In this paper, we are going to find repetitive patterns in a given motion signal without prior knowledge about the type of motion. It is relatively easier to find repetitive patterns in discrete signal that contains a limited number of states by dynamic programming. However, it is impractical to identify exactly matched states in a continuous signal such as captured human motion data. A point cloud similarity of the input motion signal itself is considered and the longest similar patterns are located by tracing and extending matched posture pairs. Through pattern alignment and auto-clustering, cyclic and acyclic patterns are identified. Experiment results show that our approach can locate repetitive movements with small error rates.
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Jeff K. T. Tang, Howard Leung, Taku Komura and Hubert P. H. Shum, "Finding Repetitive Patterns in 3D Human Motion Captured Data," in ICUIMC '08: Proceedings of the 2008 International Conference on Ubiquitous Information Management and Communication, pp. 396-403, Suwon, Korea, ACM, Jan 2008.
Last updated on 24 February 2024