Learning visual object detection and localisation using icVision

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.

[1]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[2]  Giulio Sandini,et al.  A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents , 2007, IEEE Transactions on Evolutionary Computation.

[3]  Jochen Triesch,et al.  Let it Learn - A Curious Vision System for Autonomous Object Learning , 2013, VISAPP.

[4]  E. Gibson.  Principles of Perceptual Learning and Development , 1969 .

[5]  Jürgen Leitner,et al.  Mars terrain image classification using Cartesian genetic programming , 2012 .

[6]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[7]  PoggioTomaso,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007 .

[8]  David Vernon,et al.  The iCub Cognitive Architecture , 2010 .

[9]  J. Wensveen,et al.  EYE, BRAIN, AND VISION , 1995 .

[10]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[11]  Jürgen Leitner,et al.  Cartesian Genetic Programming for Image Processing , 2013 .

[12]  D. Pollen,et al.  Phase relationships between adjacent simple cells in the visual cortex. , 1981, Science.

[13]  Nikolaos G. Tsagarakis,et al.  iCub: the design and realization of an open humanoid platform for cognitive and neuroscience research , 2007, Adv. Robotics.

[14]  Jürgen Leitner,et al.  The Modular Behavioral Environment for Humanoids and other Robots (MoBeE) , 2012, ICINCO.

[15]  Giorgio Metta,et al.  YARP: Yet Another Robot Platform , 2006 .

[16]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[17]  H. Ruff.  Infants' Manipulative Exploration of Objects: Effects of Age and Object Characteristics. , 1984 .

[18]  B. Matthews.  Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[19]  Mikhail Frank,et al.  Learning spatial object localization from vision on a humanoid robot , 2012 .

[20]  Wolfgang Banzhaf,et al.  Image Processing and CGP , 2011, Cartesian Genetic Programming.

[21]  M. Maggini,et al.  Recursive neural networks for object detection , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[22]  David J. Heeger,et al.  Neuronal correlates of perception in early visual cortex , 2003, Nature Neuroscience.

[23]  Terence Soule,et al.  Genetic Programming: Theory and Practice , 2003 .

[24]  Jürgen Leitner,et al.  Autonomous learning of robust visual object detection and identification on a humanoid , 2012, 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL).

[25]  Jürgen Leitner,et al.  Humanoid learns to detect its own hands , 2013, 2013 IEEE Congress on Evolutionary Computation.

[26]  S. Sutherland.  Eye, brain and vision , 1993, Nature.

[27]  J. P. Jones,et al.  An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

[28]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[30]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[31]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Giulio Sandini,et al.  The iCub humanoid robot: An open-systems platform for research in cognitive development , 2010, Neural Networks.

[33]  J. Miller.  An empirical study of the efficiency of learning boolean functions using a Cartesian Genetic Programming approach , 1999 .

[34]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[35]  G. Sandini,et al.  The iCub cognitive architecture: Interactive development in a humanoid robot , 2007, 2007 IEEE 6th International Conference on Development and Learning.

[36]  Jürgen Leitner,et al.  MT-CGP: mixed type cartesian genetic programming , 2012, GECCO '12.

[37]  Julian Francis Miller,et al.  Cartesian genetic programming , 2000, GECCO '10.

[38]  E. Gibson,et al.  Principles of Perceptual Learning and Development , 1973 .

[39]  Jason H. Moore,et al.  Genetic Programming Theory and Practice X , 2013, Genetic and Evolutionary Computation.

[40]  Dan Roth,et al.  Learning a Sparse Representation for Object Detection , 2002, ECCV.

[41]  Julian Francis Miller,et al.  Redundancy and computational efficiency in Cartesian genetic programming , 2006, IEEE Transactions on Evolutionary Computation.

[42]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[43]  Jürgen Leitner,et al.  Transferring spatial perception between robots operating in a shared workspace , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[44]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.