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Machine-Learning-Based Visual Objects’ Distances Evaluation: A Comparison of ANFIS, MLP, SVR and Bilinear Interpolation Models

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Computational Intelligence (IJCCI 2015)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 669))

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Abstract

Spatial characterization of objects is a key-step for robots’ awareness about the surrounding environment in which they are supposed to evolve and for their autonomy within that environment. Within this context, this chapter deals with visual evaluation of objects’ distances using Soft-Computing based approaches and pseudo-3D standard low-cost sensor, namely the Kinect. However, although presenting appealing advantages for indoor environment’s perception, the Kinect has not been designed for metrological aims. The investigated approach offers the possibility to use this low-cost pseudo-3D sensor for the aforementioned purpose by avoiding 3D feature extraction and thus exploiting the simplicity of the only 2D image’ processing. Experimental results show the viability of the proposed approach and provide comparison between different Machine-Learning techniques as Adaptive-network-based fuzzy inference (ANFIS), Multi-layer Perceptron (MLP), Support vector regression (SVR) and Bilinear Interpolation (BLI).

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Authors and Affiliations

  1. Images, Signals and Intelligence Systems Laboratory (LISSI/EA 3956), Senart-FB Institute of Technology, University PARIS-EST Creteil (UPEC), Bât.A, 36-37 Rue Charpak, 77127, Lieusaint, France

    Kurosh Madani, Hossam Fraihat & Christophe Sabourin

Authors
  1. Kurosh Madani
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  2. Hossam Fraihat
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  3. Christophe Sabourin
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Corresponding author

Correspondence to Kurosh Madani .

Editor information

Editors and Affiliations

  1. Computer Architecture and Computer Technology, Universidad de Granada, Granada, Spain

    Juan Julián Merelo

  2. aSEEB-ISR-IST, Technical University of Lisbon (IST), Lisbon, Portugal

    Agostinho Rosa

  3. Facultad de Informática, University of Murcia, Murcia, Spain

    José M. Cadenas

  4. Departamento de Engenharia Informatica, University of Coimbra, Coimbra, Portugal

    António Dourado Correia

  5. Images, Signals and Intelligence Systems Laboratory, University PARIS-EST Creteil (UPEC), Créteil, France

    Kurosh Madani

  6. Campus de Gambelas, Universidade do Algarve, Faro, Portugal

    António Ruano

  7. Escola Superior de Tecnologia de Setúbal, Polytechnic Institute of Setúbal/INSTICC, Setúbal, Portugal

    Joaquim Filipe

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Madani, K., Fraihat, H., Sabourin, C. (2017). Machine-Learning-Based Visual Objects’ Distances Evaluation: A Comparison of ANFIS, MLP, SVR and Bilinear Interpolation Models. In: Merelo, J.J., et al. Computational Intelligence. IJCCI 2015. Studies in Computational Intelligence, vol 669. Springer, Cham. https://doi.org/10.1007/978-3-319-48506-5_24

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  • DOI: https://doi.org/10.1007/978-3-319-48506-5_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48504-1

  • Online ISBN: 978-3-319-48506-5

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