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Color Point Pair Feature Light

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Advances in Visual Computing (ISVC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13018))

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Abstract

Object recognition in the field of computer vision is a constant challenge to achieve better precision in less time. In this research, is proposed a new 3D descriptor, to work with depth cameras called CPPFL, based on the PPF descriptor from [1]. This proposed descriptor takes advantage of color information and groups it more effectively and lightly than the CPPF descriptor from [2], which uses the color information too. Also, it is proposed as an alternative descriptor called CPPFL+, which differs in the construction taking advantage of the same concept of grouping colors, so it gains a “plus” in speed. This change makes the descriptor more efficient compared to PPF and CPPF descriptors. Optimizing the object recognition process [3], it can reach a rate of ten frames per second or more depending on the size of the object. The proposed descriptor is more tolerant to illuminations changes since the hue of a color is more relevant than the other components.

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Aknowledgement

M. E. LOAIZA acknowledges the financial support of the CONCYTEC – BANCO MUNDIAL Project “Mejoramiento y Ampliación de los Servicios del Sistema Nacional de Ciencia Tecnología e Innovación Tecnológica” 8682-PE, through its executing unit PROCIENCIA, within the framework of the call E041-01, Contract No. 038-2018-FONDECYT-BM-IADT-AV.

Author information

Authors and Affiliations

  1. Universidad Nacional San Agustín, Arequipa, Peru

    Luis Ronald Istaña Chipana

  2. Department of Computer Science, Universidad Católica San Pablo, Arequipa, Peru

    Luis Ronald Istaña Chipana & Manuel Eduardo Loaiza Fernández

Authors
  1. Luis Ronald Istaña Chipana
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  2. Manuel Eduardo Loaiza Fernández
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Corresponding author

Correspondence to Luis Ronald Istaña Chipana .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. University of Texas at Arlington, Arlington, TX, USA

    Vassilis Athitsos

  3. University of South Carolina, Columbia, SC, USA

    Tong Yan

  4. City University of Hong Kong, Kowloon, Hong Kong

    Manfred Lau

  5. School of Engineering and Computing, University of Durham, Durham, Durham, UK

    Frederick Li

  6. Airbnb, New York, NY, USA

    Conglei Shi

  7. Peking University, Beijing, China

    Xiaoru Yuan

  8. Purdue University, West Lafayette, IN, USA

    Christos Mousas

  9. IST, School of Modeling, Simulation, and Training, Orlando, FL, USA

    Gerd Bruder

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Istaña Chipana, L.R., Loaiza Fernández, M.E. (2021). Color Point Pair Feature Light. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2021. Lecture Notes in Computer Science(), vol 13018. Springer, Cham. https://doi.org/10.1007/978-3-030-90436-4_28

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  • DOI: https://doi.org/10.1007/978-3-030-90436-4_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90435-7

  • Online ISBN: 978-3-030-90436-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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