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• W2914795672 abstract "Videos are spatio-temporally rich in static to dynamic objects/scenes, sparse to dense, and periodic to non-periodic motions. Particularly, the dynamic texture (DT) exhibits complex appearance and motion changes that remain a challenge to deal with. This paper presents an energy optimization method for feature extraction and recognition in videos. For noise and background jitter, the Tikhonov regularization with eigen-vector and Frenet-Serret formula-based energy constraints is also proposed. The different periodicity of DT can be adapted by the time-varying number of learning temporal frames. The optimal duration of an image sequence is determined from the temporal property of its eigen-values. Unlike the state-of-the-art recognition methods, i.e., sparse coding and slow feature analysis, the proposed method can capture the physical property of objects and scenes: velocity, acceleration, and orientation. Also, the static and dynamic image regions can be locally classified. Owing to these spatio-temporal features, stability, robustness, and accuracy of feature extraction and recognition are enhanced. Using DT videos, the superiority of the proposed method compared to the state-of-the-art recognition methods is experimentally shown." @default.
• W2914795672 created "2019-02-21" @default.
• W2914795672 creator A5023264894 @default.
• W2914795672 date "2019-07-01" @default.
• W2914795672 modified "2023-09-25" @default.
• W2914795672 title "Spatio-Temporal Feature Extraction/Recognition in Videos Based on Energy Optimization" @default.
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• W2914795672 doi "https://doi.org/10.1109/tip.2019.2896529" @default.
• W2914795672 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30714924" @default.
• W2914795672 hasPublicationYear "2019" @default.
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