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Complex-Valued Representation for RGB-D Object Recognition

  • Rim Trabelsi
  • Issam Jabri
  • Farid Melgani
  • Fethi Smach
  • Nicola Conci
  • Ammar Bouallegue
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10749)

Abstract

Object recognition methods usually tend to focus on single cues coming from traditional vision based systems but ignore to incorporate multi-modal data. With the advent of depth RGB-D sensors which provide synchronized multi-modal data with good quality, new opportunities have been emerged. In this paper, we make use of RGB and depth images to propose a new object recognition approach. Using a pixel-wise scheme, we propose a novel method to describe RGB-D images with a complex-valued representation. By means of neural network, we introduce a new CVNN (Complex-Valued Neural Network) with RBF neurons. Different from many RGB-D features, the proposed approach is able to jointly use RGB and depth data within a unified end-to-end learning framework. Category and instance object recognition tasks are evaluated through experiments carried out on a large scale RGB-D object dataset. Results show that our method can efficiently recognize objects in RGB-D images and outperforms state-of-the-art approaches.

Keywords

RGB-D representation Object recognition Complex-valued neural networks Multi-modal data 

Notes

Acknowledgements

This work was supported by the European Union funding through ALYSSA program (ERASMUS-MUNDUS action 2 lot 6) and by the research grant from Singapore Agency for Science, Technology and Research (A*STAR) through the ARAP program.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rim Trabelsi
    • 1
    • 2
    • 3
  • Issam Jabri
    • 4
  • Farid Melgani
    • 5
  • Fethi Smach
    • 6
  • Nicola Conci
    • 5
  • Ammar Bouallegue
    • 2
  1. 1.Advanced Digital Sciences CenterSingaporeSingapore
  2. 2.SysCom Laboratory, National Engineering School of TunisUniversity of Tunis El ManarTunisTunisia
  3. 3.Hatem Bettaher IResCoMath Research Unit, National Engineering School of GabesUniversity of GabesGabèsTunisia
  4. 4.College of Computer and Information SystemsAl Yamamah UniversityRiyadhKingdom of Saudi Arabia
  5. 5.Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly
  6. 6.Profil TechnologyMontrougeFrance

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