Learning visual object detection and localisation using icVision

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Juergen Leitner and Simon Harding and 
                 Pramod Chandrashekhariah and Mikhail Frank and 
                 Alexander Foerster and Jochen Triesch and Juergen Schmidhuber",
  title =        "Learning visual object detection and localisation
                 using icVision",
  journal =      "Biologically Inspired Cognitive Architectures",
  year =         "2013",
  volume =       "5",
  pages =        "29--41",
  month =        jul,
  note =         "Extended versions of selected papers from the Third
                 Annual Meeting of the BICA Society (BICA 2012)",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, Humanoid robots, iCub,
                 cartesianController, Computer vision, Perception,
                 Cognition, Learning, Object detection",
  ISSN =         "2212-683X",
  URL =          "http://www.sciencedirect.com/science/article/pii/S2212683X13000443",
  DOI =          "doi:10.1016/j.bica.2013.05.009",
  size =         "13 pages",
  abstract =     "Building artificial agents and robots that can act in
                 an intelligent way is one of the main research goals in
                 artificial intelligence and robotics. Yet it is still
                 hard to integrate functional cognitive processes into
                 these systems. We present a framework combining
                 computer vision and machine learning for the learning
                 of object recognition in humanoid robots. A
                 biologically inspired, bottom-up architecture is
                 introduced to facilitate visual perception and
                 cognitive robotics research. It aims to mimic processes
                 in the human brain performing visual cognition tasks. A
                 number of experiments with this icVision framework are
                 described. We showcase both detection and
                 identification in the image plane (2D), using machine
                 learning. In addition we show how a biologically
                 inspired attention mechanism allows for fully
                 autonomous learning of visual object representations.
                 Furthermore localising the detected objects in 3D space
                 is presented, which in turn can be used to create a
                 model of the environment.",
  notes =        "a Dalle Molle Institute for Artificial Intelligence
                 (IDSIA)/SUPSI/USI, Manno-Lugano, Switzerland b Machine
                 Intelligence Ltd., South Zeal, United Kingdom c
                 Frankfurt Institute of Advanced Studies (FIAS),
                 Frankfurt am Main, Germany

                 Also known as \cite{Leitner201329}",

Genetic Programming entries for Juergen Leitner Simon Harding Pramod Chandrashekhariah Mikhail Frank Alexander Forster Jochen Triesch Jurgen Schmidhuber