• DocumentCode
    187150
  • Title

    Methodology for the objects recognition in range images using Kinect sensor

  • Author

    Alzate, Anderson ; Gallego, Carlos A. ; Uribe, Juan D. ; Madrigal, Carlos A. ; Villegas, Juan P.

  • Author_Institution
    Eng. Fac., Metropolitan Technol. Inst. ITM, Medellín, Colombia
  • fYear
    2014
  • fDate
    22-24 Oct. 2014
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    This paper presents the methodology used for the detection and recognition of some objects from the analysis of range images obtained from the Kinect sensor. Thus, is carried out the integration of a number of methodologies that are used daily in processes of vision and artificial intelligence, and from these, are intended to illustrate how efficient it can be the right choice of existing methods for the best results. In the segmentation stage it has been used the well-known Maximally-stable Extremal Region Extraction Algorithm, then, geometric characteristics of each object are extracted, and an evaluation is made of different classifiers. After the experimental tests it was possible to obtain a recognition rate of 99.4% through a multilayer perceptron neural network. All processing is performed using the openCV and openNI libraries.
  • Keywords
    feature extraction; image segmentation; image sensors; multilayer perceptrons; object recognition; Kinect sensor; artificial intelligence; geometric characteristics; maximally-stable extremal region extraction algorithm; multilayer perceptron neural network; objects recognition; openCV; openNI libraries; range images; segmentation stage; Feature extraction; Image recognition; Image segmentation; Neurons; Object recognition; Robot sensing systems; Kinect sensor; MSER-Maximally-stable Extremal Region Extraction; artificial intelligence; multilayer perceptron neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering Mechatronics and Automation (CIIMA), 2014 III International Congress of
  • Conference_Location
    Cartagena
  • Print_ISBN
    978-1-4799-7931-8
  • Type

    conf

  • DOI
    10.1109/CIIMA.2014.6983450
  • Filename
    6983450