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Atomic Action

The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform

Hong-yuan Mark Liao – 1st expert on this subject based on the ideXlab platform

  • Unsupervised analysis of human behavior based on manifold learning
    2009 IEEE International Symposium on Circuits and Systems, 2009
    Co-Authors: Yu-ming Liang, Arthur Chun-chieh Shih, Sheng-wen Shih, Hong-yuan Mark Liao

    Abstract:

    In this paper, we propose a framework for unsupervised analysis of human behavior based on manifold learning. First, a pairwise human posture distance matrix is calculated from a training Action sequence. Then, the isometric feature mapping (Isomap) algorithm is applied to construct a low-dimensional structure from the distance matrix. The data points in the Isomap space are consequently represented as a time-series of low-dimensional points. A temporal segmentation technique is then applied to segment the time series into subseries corresponding to Atomic Actions. Next, a dynamic time warping (DTW) approach is applied for clustering Atomic Action sequences. Finally, we use the clustering results to learn and classify Atomic Actions using the nearest neighbor rule. Experiments conducted on real data demonstrate the efficacy of the proposed method.

  • ISCAS – Unsupervised analysis of human behavior based on manifold learning
    2009 IEEE International Symposium on Circuits and Systems, 2009
    Co-Authors: Yu-ming Liang, Arthur Chun-chieh Shih, Sheng-wen Shih, Hong-yuan Mark Liao

    Abstract:

    In this paper, we propose a framework for unsupervised analysis of human behavior based on manifold learning. First, a pairwise human posture distance matrix is calculated from a training Action sequence. Then, the isometric feature mapping (Isomap) algorithm is applied to construct a low-dimensional structure from the distance matrix. The data points in the Isomap space are consequently represented as a time-series of low-dimensional points. A temporal segmentation technique is then applied to segment the time series into subseries corresponding to Atomic Actions. Next, a dynamic time warping (DTW) approach is applied for clustering Atomic Action sequences. Finally, we use the clustering results to learn and classify Atomic Actions using the nearest neighbor rule. Experiments conducted on real data demonstrate the efficacy of the proposed method.

  • Learning Atomic Human Actions Using Variable-Length Markov Models
    IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2009
    Co-Authors: Yu-ming Liang, Arthur Chun-chieh Shih, Sheng-wen Shih, Hong-yuan Mark Liao

    Abstract:

    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.

Hai Zhou – 2nd expert on this subject based on the ideXlab platform

  • ASP-DAC – Synthesis of resilient circuits from guarded Atomic Actions
    The 20th Asia and South Pacific Design Automation Conference, 2016
    Co-Authors: Yuankai Chen, Hai Zhou

    Abstract:

    With aggressive scaling of minimum feature sizes, supply voltages, and design guard-bands, transient faults have become critical issues in modern electronic circuits. Synthesis from guarded Atomic Actions has been investigated by Arvind et al. to explore non-determinism for hardware concurrency. We show in this work that non-determinism in the guarded Atomic Actions can be further explored for synthesis of resilient circuits. When an error happens in an Atomic Action, the Action may not need to be recomputed if there exist other feasible Actions. Such flexibilities will be increased in the specification and explored in the synthesis for efficient error resiliency. Our synthesis approach expands the solution space and offers the possibility of performance optimization. Experimental results demonstrate the effectiveness and efficiency of our synthesis approach.

  • Synthesis of resilient circuits from guarded Atomic Actions
    The 20th Asia and South Pacific Design Automation Conference, 2015
    Co-Authors: Yuankai Chen, Hai Zhou

    Abstract:

    With aggressive scaling of minimum feature sizes, supply voltages, and design guard-bands, transient faults have become critical issues in modern electronic circuits. Synthesis from guarded Atomic Actions has been investigated by Arvind et al. to explore non-determinism for hardware concurrency. We show in this work that non-determinism in the guarded Atomic Actions can be further explored for synthesis of resilient circuits. When an error happens in an Atomic Action, the Action may not need to be recomputed if there exist other feasible Actions. Such flexibilities will be increased in the specification and explored in the synthesis for efficient error resiliency. Our synthesis approach expands the solution space and offers the possibility of performance optimization. Experimental results demonstrate the effectiveness and efficiency of our synthesis approach.

Yu-ming Liang – 3rd expert on this subject based on the ideXlab platform

  • Human Action segmentation and classification based on the Isomap algorithm
    Multimedia Tools and Applications, 2013
    Co-Authors: Yu-ming Liang, Sheng-wen Shih, Arthur Chun-chieh Shih

    Abstract:

    Visual analysis of human behavior has attracted a great deal of attention in the field of computer vision because of the wide variety of potential applications. Human behavior can be segmented into Atomic Actions, each of which indicates a single, basic movement. To reduce human intervention in the analysis of human behavior, unsupervised learning may be more suitable than supervised learning. However, the complex nature of human behavior analysis makes unsupervised learning a challenging task. In this paper, we propose a framework for the unsupervised analysis of human behavior based on manifold learning. First, a pairwise human posture distance matrix is derived from a training Action sequence. Then, the isometric feature mapping (Isomap) algorithm is applied to construct a low-dimensional structure from the distance matrix. Consequently, the training Action sequence is mapped into a manifold trajectory in the Isomap space. To identify the break points between the trajectories of any two successive Atomic Actions, we represent the manifold trajectory in the Isomap space as a time series of low-dimensional points. A temporal segmentation technique is then applied to segment the time series into sub series, each of which corresponds to an Atomic Action. Next, the dynamic time warping (DTW) approach is used to cluster Atomic Action sequences. Finally, we use the clustering results to learn and classify Atomic Actions according to the nearest neighbor rule. If the distance between the input sequence and the nearest mean sequence is greater than a given threshold, it is regarded as an unknown Atomic Action. Experiments conducted on real data demonstrate the effectiveness of the proposed method.

  • Unsupervised analysis of human behavior based on manifold learning
    2009 IEEE International Symposium on Circuits and Systems, 2009
    Co-Authors: Yu-ming Liang, Arthur Chun-chieh Shih, Sheng-wen Shih, Hong-yuan Mark Liao

    Abstract:

    In this paper, we propose a framework for unsupervised analysis of human behavior based on manifold learning. First, a pairwise human posture distance matrix is calculated from a training Action sequence. Then, the isometric feature mapping (Isomap) algorithm is applied to construct a low-dimensional structure from the distance matrix. The data points in the Isomap space are consequently represented as a time-series of low-dimensional points. A temporal segmentation technique is then applied to segment the time series into subseries corresponding to Atomic Actions. Next, a dynamic time warping (DTW) approach is applied for clustering Atomic Action sequences. Finally, we use the clustering results to learn and classify Atomic Actions using the nearest neighbor rule. Experiments conducted on real data demonstrate the efficacy of the proposed method.

  • ISCAS – Unsupervised analysis of human behavior based on manifold learning
    2009 IEEE International Symposium on Circuits and Systems, 2009
    Co-Authors: Yu-ming Liang, Arthur Chun-chieh Shih, Sheng-wen Shih, Hong-yuan Mark Liao

    Abstract:

    In this paper, we propose a framework for unsupervised analysis of human behavior based on manifold learning. First, a pairwise human posture distance matrix is calculated from a training Action sequence. Then, the isometric feature mapping (Isomap) algorithm is applied to construct a low-dimensional structure from the distance matrix. The data points in the Isomap space are consequently represented as a time-series of low-dimensional points. A temporal segmentation technique is then applied to segment the time series into subseries corresponding to Atomic Actions. Next, a dynamic time warping (DTW) approach is applied for clustering Atomic Action sequences. Finally, we use the clustering results to learn and classify Atomic Actions using the nearest neighbor rule. Experiments conducted on real data demonstrate the efficacy of the proposed method.