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- W4385804917 abstract "Optical flow estimation is a fundamental functionality in computer vision. An event-based camera, which asynchronously detects sparse intensity changes, is an ideal device for realizing low-latency estimation of the optical flow owing to its low-latency sensing mechanism. We developed an efficient full-flow estimation called Tangentially elongated Gaussian belief propagation (TEGBP). TEGBP formulates the full flow estimation as the marginalization of probability using a message-passing based on the BP. The formulation permits event-by-event asynchronous incremental updates of the full flow; i.e., given a normal-flow observation, it updates its belief about full flow by asynchronous local communication. This paper presents a OpenMP based real-time full-flow estimation demo by taking advantage of the asynchronous formulation. Specifically, we parallelize the individual sequence of the message exchange evoked by a single normal-flow observation. Beliefs at each node are updated on an event-by-event basis manner in parallel, realizing the real-time procession on CPUs. Our C++ code is available at https://github.com/DensoITLab/tegbp." @default.
- W4385804917 created "2023-08-15" @default.
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- W4385804917 date "2023-06-01" @default.
- W4385804917 modified "2023-10-01" @default.
- W4385804917 title "Live Demonstration: Tangentially Elongated Gaussian Belief Propagation for Event-based Incremental Optical Flow Estimation" @default.
- W4385804917 cites W2785582094 @default.
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- W4385804917 doi "https://doi.org/10.1109/cvprw59228.2023.00408" @default.
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