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Moving skin and shadow region analysis via adaptive models

Published: 17 August 2013 Publication History

Abstract

In this work, moving skin and shadow regions are analyzed and detected using adaptive mixture models. Both motion information and clues from skin and shadow properties are used to construct and update the models. The skin and shadow probabilities of each pixel are integrated into the mixture model framework. Furthermore, the adaptive mixtures are updated dynamically with learning rates adjusted by the skin and shadow probabilities. The experimental results have validated the effectiveness of the proposed method.

References

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Deng, Y. and Wu, L. 2012. A Novel Approach to Analyzing Object Motion Behavior. Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (July 2012), 395--398.
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Meisner, E. M., Àbanovic, S., Isler, V., Caporeal, L. R., and Trinkle, J. 2009. ShadowPlay: A Generative Model for Nonverbal Human-Robot Interaction. 4th ACM/IEEE international conference on Human robot interaction, 117--124.
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Stauffer, C. and Grimson, W. E. L 1999. Adaptive background mixture models for real-time tracking. Proc. Conf. Computer Vision and Pattern Recognition 2, 246--252.
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Martel-Brisson, N. and Zaccarin, A. 2007 Learning and removing cast shadows through a multidistribution approach. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1133--1146.
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Chaves-González, J. M. and Vega-Rodríguez, M. A. J. A. 2010. Gómez-Pulido. Detecting skin in face recognition systems: A colour spaces study, Digital Signal Processing 20, 806--823.
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Phung, S. L., Bouzerdoum, A., and Chai, D. 2005. Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans. Pattern Anal. Mach. Intell. 27, 148--154.

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  1. Moving skin and shadow region analysis via adaptive models

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    ICIMCS '13: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
    August 2013
    419 pages
    ISBN:9781450322522
    DOI:10.1145/2499788
    • Conference Chair:
    • Tat-Seng Chua,
    • General Chairs:
    • Ke Lu,
    • Tao Mei,
    • Xindong Wu
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    • NSF of China: National Natural Science Foundation of China
    • University of Sciences & Technology, Hefei: University of Sciences & Technology, Hefei
    • Beijing ACM SIGMM Chapter

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 17 August 2013

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    Author Tags

    1. adaptive models
    2. moving skin region detection
    3. shadow detection

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    ICIMCS '13
    Sponsor:
    • NSF of China
    • University of Sciences & Technology, Hefei

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    ICIMCS '13 Paper Acceptance Rate 20 of 94 submissions, 21%;
    Overall Acceptance Rate 163 of 456 submissions, 36%

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