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3D Object Categorization in Cluttered Scene Using Deep Belief Network Architectures

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Nature-Inspired Computation in Data Mining and Machine Learning

Part of the book series: Studies in Computational Intelligence ((SCI,volume 855))

Abstract

3D object classification in cluttered scenes is a critical area of computer vision and robotic research for autonomous robots to act in their surrounding area. In this chapter, we extend our previous work [51] by classifying 3D object categories in real-world scenes. We extract geometric features from 3D point clouds using a 3D global descriptor called Viewpoint Feature Histogram (VFH) then we learn the extracted features with Deep Belief Networks (DBNs). Thereafter, we test the power of Discriminative and Generative DBN architectures (DDBN/GDBN) for object categorization. The experiments on Washington RGBD dataset demonstrate the robustness of discriminative architecture which outperforms state-of-the-art. Also, we evaluate the performance of our approach on the real-world objects that are segmented from cluttered indoor scenes.

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Authors and Affiliations

  1. LIMIARF FSR Mohammed V University in Rabat, Rabat, Morocco

    Nabila Zrira, Mohamed Hannat & El Houssine Bouyakhf

Authors
  1. Nabila Zrira
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  2. Mohamed Hannat
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  3. El Houssine Bouyakhf
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Correspondence to Nabila Zrira .

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Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

  2. College of Science, Xi’an Polytechnic University, Xi’an, China

    Xing-Shi He

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Zrira, N., Hannat, M., Bouyakhf, E.H. (2020). 3D Object Categorization in Cluttered Scene Using Deep Belief Network Architectures. In: Yang, XS., He, XS. (eds) Nature-Inspired Computation in Data Mining and Machine Learning. Studies in Computational Intelligence, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-030-28553-1_8

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