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Human Object Classification Based on Nonsubsampled Contourlet Transform Combined with Zernike Moment

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Context-Aware Systems and Applications (ICCASA 2015)

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Abstract

The surveillance systems are more and more popular because of the security needs, but the traditional ones do not meet human’s expectation. This paper proposes the algorithm to classify objects mainly based on their contour property which are represented by the amplitude of zernike moment on nonsubsampled contourlet transform of a binary contour image. This feature shows promising results by just a simple association with the aspect ratio but gives high accuracy. The aspect ratio helps contour feature in case that the image is too blurred to extract the object’s contour. It also plays as a weak filter with nearly no more computational cost except for a division to support contour feature when applying gentle boost algorithm.

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Acknowledgments

This research is funded by Ho Chi Minh City University of Technology, VNU-HCM under grant number TSĐH-2015-KHMT-07

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, VNU-HCM, Vietnam

    Luu The Phuong & Nguyen Thanh Binh

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  1. Luu The Phuong
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  2. Nguyen Thanh Binh
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Correspondence to Nguyen Thanh Binh .

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Editors and Affiliations

  1. Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam

    Phan Cong Vinh

  2. Concordia University, Montréal, Québec, Canada

    Vangalur Alagar

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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Phuong, L.T., Binh, N.T. (2016). Human Object Classification Based on Nonsubsampled Contourlet Transform Combined with Zernike Moment. In: Vinh, P., Alagar, V. (eds) Context-Aware Systems and Applications. ICCASA 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 165. Springer, Cham. https://doi.org/10.1007/978-3-319-29236-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-29236-6_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29235-9

  • Online ISBN: 978-3-319-29236-6

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