The Experts below are selected from a list of 42 Experts worldwide ranked by ideXlab platform
Hong-yuan Mark Liao - One of the best experts on this subject based on the ideXlab platform.
-
Learning Atomic Human Actions Using Variable-Length Markov Models
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2009Co-Authors: Yu-ming Liang, Arthur Chun-chieh Shih, Sheng-wen Shih, Hong-yuan Mark LiaoAbstract:Visual analysis of human behavior has generated considerable interest in the field of computer vision because of its wide spectrum of potential applications. Human behavior can be segmented into atomic actions, each of which indicates a basic and complete movement. Learning and recognizing atomic human actions are essential to human behavior analysis. In this paper, we propose a framework for handling this task using variable-length Markov models (VLMMs). The framework is comprised of the following two modules: a posture labeling module and a VLMM atomic action learning and recognition module. First, a posture template selection algorithm, based on a modified shape context matching technique, is developed. The selected posture templates form a codebook that is used to convert input posture sequences into Discrete Symbol sequences for subsequent processing. Then, the VLMM technique is applied to learn the training Symbol sequences of atomic actions. Finally, the constructed VLMMs are transformed into hidden Markov models (HMMs) for recognizing input atomic actions. This approach combines the advantages of the excellent learning function of a VLMM and the fault-tolerant recognition ability of an HMM. Experiments on realistic data demonstrate the efficacy of the proposed system.
Yu-ming Liang - One of the best experts on this subject based on the ideXlab platform.
-
Learning Atomic Human Actions Using Variable-Length Markov Models
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2009Co-Authors: Yu-ming Liang, Arthur Chun-chieh Shih, Sheng-wen Shih, Hong-yuan Mark LiaoAbstract:Visual analysis of human behavior has generated considerable interest in the field of computer vision because of its wide spectrum of potential applications. Human behavior can be segmented into atomic actions, each of which indicates a basic and complete movement. Learning and recognizing atomic human actions are essential to human behavior analysis. In this paper, we propose a framework for handling this task using variable-length Markov models (VLMMs). The framework is comprised of the following two modules: a posture labeling module and a VLMM atomic action learning and recognition module. First, a posture template selection algorithm, based on a modified shape context matching technique, is developed. The selected posture templates form a codebook that is used to convert input posture sequences into Discrete Symbol sequences for subsequent processing. Then, the VLMM technique is applied to learn the training Symbol sequences of atomic actions. Finally, the constructed VLMMs are transformed into hidden Markov models (HMMs) for recognizing input atomic actions. This approach combines the advantages of the excellent learning function of a VLMM and the fault-tolerant recognition ability of an HMM. Experiments on realistic data demonstrate the efficacy of the proposed system.
Takao Kobayashi - One of the best experts on this subject based on the ideXlab platform.
-
hidden markov models based on multi space probability distribution for pitch pattern modeling
International Conference on Acoustics Speech and Signal Processing, 1999Co-Authors: Keiichi Tokuda, Takashi Masuko, Noboru Miyazaki, Takao KobayashiAbstract:This paper discusses a hidden Markov model (HMM) based on multi-space probability distribution (MSD). The HMMs are widely-used statistical models to characterize the sequence of speech spectra and have successfully been applied to speech recognition systems. From these facts, it is considered that the HMM is useful for modeling pitch patterns of speech. However, we cannot apply the conventional Discrete or continuous HMMs to pitch pattern modeling since the observation sequence of the pitch pattern is composed of one-dimensional continuous values and a Discrete Symbol which represents "unvoiced". MSD-HMM includes Discrete HMMs and continuous mixture HMMs as special cases, and further can model the sequence of observation vectors with variable dimension including zero-dimensional observations, i.e., Discrete Symbols. As a result, MSD-HMMs can model pitch patterns without heuristic assumption. We derive a reestimation algorithm for the extended HMM and show that it can find a critical point of the likelihood function.
Arthur Chun-chieh Shih - One of the best experts on this subject based on the ideXlab platform.
-
Learning Atomic Human Actions Using Variable-Length Markov Models
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2009Co-Authors: Yu-ming Liang, Arthur Chun-chieh Shih, Sheng-wen Shih, Hong-yuan Mark LiaoAbstract:Visual analysis of human behavior has generated considerable interest in the field of computer vision because of its wide spectrum of potential applications. Human behavior can be segmented into atomic actions, each of which indicates a basic and complete movement. Learning and recognizing atomic human actions are essential to human behavior analysis. In this paper, we propose a framework for handling this task using variable-length Markov models (VLMMs). The framework is comprised of the following two modules: a posture labeling module and a VLMM atomic action learning and recognition module. First, a posture template selection algorithm, based on a modified shape context matching technique, is developed. The selected posture templates form a codebook that is used to convert input posture sequences into Discrete Symbol sequences for subsequent processing. Then, the VLMM technique is applied to learn the training Symbol sequences of atomic actions. Finally, the constructed VLMMs are transformed into hidden Markov models (HMMs) for recognizing input atomic actions. This approach combines the advantages of the excellent learning function of a VLMM and the fault-tolerant recognition ability of an HMM. Experiments on realistic data demonstrate the efficacy of the proposed system.
Sheng-wen Shih - One of the best experts on this subject based on the ideXlab platform.
-
Learning Atomic Human Actions Using Variable-Length Markov Models
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2009Co-Authors: Yu-ming Liang, Arthur Chun-chieh Shih, Sheng-wen Shih, Hong-yuan Mark LiaoAbstract:Visual analysis of human behavior has generated considerable interest in the field of computer vision because of its wide spectrum of potential applications. Human behavior can be segmented into atomic actions, each of which indicates a basic and complete movement. Learning and recognizing atomic human actions are essential to human behavior analysis. In this paper, we propose a framework for handling this task using variable-length Markov models (VLMMs). The framework is comprised of the following two modules: a posture labeling module and a VLMM atomic action learning and recognition module. First, a posture template selection algorithm, based on a modified shape context matching technique, is developed. The selected posture templates form a codebook that is used to convert input posture sequences into Discrete Symbol sequences for subsequent processing. Then, the VLMM technique is applied to learn the training Symbol sequences of atomic actions. Finally, the constructed VLMMs are transformed into hidden Markov models (HMMs) for recognizing input atomic actions. This approach combines the advantages of the excellent learning function of a VLMM and the fault-tolerant recognition ability of an HMM. Experiments on realistic data demonstrate the efficacy of the proposed system.