Enhancing object recognition for humanoid robots through time-awareness

In this paper, we present a biologically-inspired object recognition system for humanoid robots. Our approach is based on a hierarchical model of the visual cortex for feature extraction and rapid scene categorization of natural images. We enhanced the model to be entropy-aware and real-time capable, to be able to realize object recognition over time. We integrate time in our system to model uncertainty in static object recognition by evaluating multiple recognition results of objects observed at different view-points over time using the camera system on a humanoid robot. The recognition responses are encoded as probability estimates over each trained object class. We apply a signal detection theory approach to describe the temporally and spatially distributed signals to gain a value of certainty about the object class. We show that our enhanced model outperforms the preceding model and that by integrating time as a variable we created a highly robust object recognition system.

[1]  Laurent Itti,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Rapid Biologically-inspired Scene Classification Using Features Shared with Visual Attention , 2022 .

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

[3]  Gordon Cheng,et al.  Making Object Learning and Recognition an Active Process , 2008, Int. J. Humanoid Robotics.

[4]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[5]  Nuno Vasconcelos,et al.  Biologically Inspired Object Tracking Using Center-Surround Saliency Mechanisms , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Lior Wolf,et al.  Using Biologically Inspired Features for Face Processing , 2007, International Journal of Computer Vision.

[7]  Shengyong Chen,et al.  Active vision in robotic systems: A survey of recent developments , 2011, Int. J. Robotics Res..

[8]  Anthony J. Hornof,et al.  A Computational Model of “Active Vision” for Visual Search in Human–Computer Interaction , 2011, Hum. Comput. Interact..

[9]  Cheng Gordon.  An Information Theoretic Approach to an Entropy-Adaptive Neurobiologically Inspired Object Recognition Model , 2011 .

[10]  Yoseph Bar-Cohen,et al.  Biologically inspired intelligent robots , 2003, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[11]  R. Pfeifer,et al.  Self-Organization, Embodiment, and Biologically Inspired Robotics , 2007, Science.

[12]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

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

[14]  A. Watson.  Probability summation over time , 1979, Vision Research.

[15]  Sinan Kalkan,et al.  Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision? , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[17]  M. Farah.  Is an Object an Object an Object? Cognitive and Neuropsychological Investigations of Domain Specificity in Visual Object Recognition , 1992 .

[18]  Thomas Serre,et al.  Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[19]  Heiko Wersing,et al.  Active 3D Object Localization Using a Humanoid Robot , 2011, IEEE Transactions on Robotics.

[20]  Marc'Aurelio Ranzato,et al.  Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[21]  H. Deubel,et al.  Saccade target selection and object recognition: Evidence for a common attentional mechanism , 1996, Vision Research.

[22]  Kenneth D. Miller,et al.  Adaptive filtering enhances information transmission in visual cortex , 2006, Nature.

[23]  Gordon Cheng,et al.  Support vector machines and Gabor kernels for object recognition on a humanoid with active foveated vision , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[24]  Gordon Cheng,et al.  Distributed visual attention on a humanoid robot , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[25]  Lorenzo Rosasco,et al.  The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work). , 2012 .

[26]  Danica Kragic,et al.  An Active Vision System for Detecting, Fixating and Manipulating Objects in the Real World , 2010, Int. J. Robotics Res..

[27]  Li Fei-Fei,et al.  Neural mechanisms of rapid natural scene categorization in human visual cortex , 2009, Nature.

[28]  P. Perona,et al.  Rapid natural scene categorization in the near absence of attention , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[29]  J. DiCarlo,et al.  Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex , 2010, Neuron.

[30]  Cheng Gordon.  A Neurologically Motivated Computational Architecture for Real-Time Object Recognition , 2012 .

[31]  Robert L. Goldstone,et al.  Definition , 1960, A Philosopher Looks at Sport.

[32]  D. Hubel,et al.  Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.

[33]  Robert J. Wood,et al.  The First Takeoff of a Biologically Inspired At-Scale Robotic Insect , 2008, IEEE Transactions on Robotics.

[34]  Heiko Wersing,et al.  Peripersonal space and object recognition for humanoids , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..