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Issue No. 08 - Aug. (2012 vol. 34)
ISSN: 0162-8828
pp: 1645-1657
T. Tung , Dept. of Intell. Sci. & Technol., Kyoto Univ., Kyoto, Japan
T. Matsuyama , Dept. of Intell. Sci. & Technol., Kyoto Univ., Kyoto, Japan
This paper presents a novel approach that achieves 3D video understanding. 3D video consists of a stream of 3D models of subjects in motion. The acquisition of long sequences requires large storage space (2 GB for 1 min). Moreover, it is tedious to browse data sets and extract meaningful information. We propose the topology dictionary to encode and describe 3D video content. The model consists of a topology-based shape descriptor dictionary which can be generated from either extracted patterns or training sequences. The model relies on 1) topology description and classification using Reeb graphs, and 2) a Markov motion graph to represent topology change states. We show that the use of Reeb graphs as the high-level topology descriptor is relevant. It allows the dictionary to automatically model complex sequences, whereas other strategies would require prior knowledge on the shape and topology of the captured subjects. Our approach serves to encode 3D video sequences, and can be applied for content-based description and summarization of 3D video sequences. Furthermore, topology class labeling during a learning process enables the system to perform content-based event recognition. Experiments were carried out on various 3D videos. We showcase an application for 3D video progressive summarization using the topology dictionary.
video signal processing, graph theory, image recognition, image sequences, learning (artificial intelligence), Markov processes, 3D video progressive summarization, 3D video understanding, data sets, 3D video content, topology-based shape descriptor dictionary, pattern extraction, training sequences, Reeb graphs, Markov motion graph, topology change states, 3D video sequences, content-based description, learning process, content-based event recognition, Three dimensional displays, Topology, Dictionaries, Shape, Video sequences, Solid modeling, Markov processes, semantic description., 3D video, dictionary, Reeb graph, topology matching, Markov model, editing, summarization

T. Tung and T. Matsuyama, "Topology Dictionary for 3D Video Understanding," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 1645-1657, 2012.
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