Support vector machines and Gabor kernels for object recognition on a humanoid with active foveated vision

Object recognition requires a robot to perform a number of nontrivial tasks such as finding objects of interest, directing its eyes towards the objects, pursuing them, and identifying the objects once they appear in the robot's central vision. We have recently developed a recognition system on a humanoid robot, which makes use of foveated vision to accomplish these tasks (A Ude, et al., 2003). In this paper we present several substantial improvements to this system. We present a biologically motivated object representation scheme based on Gabor kernel functions and show how to employ support vector machines to identify known objects in foveal images based on this representation. A mechanism for visual search is integrated into the system to find objects of interest in peripheral images. The framework also includes a control scheme for eye movements, which are directed using the results of attentive processing in peripheral images.

[1]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Todd S. Horowitz,et al.  Visual search has no memory , 1998, Nature.

[3]  Gerald Sommer,et al.  Gabor wavelet networks for efficient head pose estimation , 2002, Image Vis. Comput..

[4]  Brian Scassellati,et al.  Eye Finding via Face Detection for a Foveated Active Vision System , 1998, AAAI/IAAI.

[5]  Rogério Schmidt Feris,et al.  Active Wavelet Networks for Face Alignment , 2003, BMVC.

[6]  D. Marr,et al.  Representation and recognition of the spatial organization of three-dimensional shapes , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[7]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

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

[9]  Mark Persoff.  UK , 1999, EC Tax Review.

[10]  Heinrich H Bülthoff,et al.  Image-based object recognition in man, monkey and machine , 1998, Cognition.

[11]  Gordon Cheng,et al.  Combining peripheral and foveal humanoid vision to detect, pursue, recognize and act , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[12]  H. Kozima,et al.  A Robot that Learns to Communicate with Human Caregivers , 2001 .

[13]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[14]  Peter I. Corke,et al.  A tutorial on visual servo control , 1996, IEEE Trans. Robotics Autom..

[15]  Brian Scassellati,et al.  Active vision for sociable robots , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[16]  Stefan Schaal,et al.  Biomimetic Oculomotor Control , 2001, Adapt. Behav..

[17]  Guodong Guo,et al.  Support vector machines for face recognition , 2001, Image Vis. Comput..

[18]  Ales Ude,et al.  Probabilistic detection and tracking at high frame rates using affine warping , 2002, Object recognition supported by user interaction for service robots.

[19]  G. Sandini,et al.  Babybot : an artificial developing robotic agent , 2000 .

[20]  Massimiliano Pontil,et al.  Support Vector Machines for 3D Object Recognition , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  V Krüger,et al.  Wavelet networks for face processing. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[22]  J. Wolfe.  Moving towards solutions to some enduring controversies in visual search , 2003, Trends in Cognitive Sciences.

[23]  P. Verghese.  Visual Search and Attention A Signal Detection Theory Approach , 2001, Neuron.

[24]  Donald Geman,et al.  An Active Testing Model for Tracking Roads in Satellite Images , 1996, IEEE Trans. Pattern Anal. Mach. Intell..