Learning feature representations for an object recognition system

Proceedings of the 2006 6th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS

Volumn , Issue , 2006, Pages 290-295

  Welke, Kai ^b,c   Oztop, Erhan ^a,b   Ude, Ales ^d   Dillmann, Rüdiger ^c   Cheng, Gordon ^a,b  

^a JAPAN SCIENCE AND TECHNOLOGY AGENCY   (Japan)

^b ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

^c UNIVERSITY OF KARLSRUHE   (Germany)

^d JO EF STEFAN INSTITUTE   (Slovenia)

Author keywords

[No Author keywords available]

Indexed keywords

DIGITAL SIGNAL PROCESSING; NEURAL NETWORKS; OBJECT RECOGNITION; ROBOTICS; ROBOTS;

COGNITIVE ARCHITECTURES; CORTICAL REPRESENTATION; DAILY LIVES; HUMANOID ROBOTS; INTERNATIONAL CONFERENCES; KEY-FRAMES; OBJECT RECOGNITION SYSTEMS; OBJECT REPRESENTATIONS; ROTATION-INVARIANT; SINGLE OBJECT; VISUAL FEATURES;

INTELLIGENT ROBOTS;

EID: 48149095514     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICHR.2006.321399     Document Type: Conference Paper

References (24)

1. A. Ude, C. Gaskett, and G. Cheng, "Support vector machines and gabor kernels for object recognition on a humanoid with active foveated vision," in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004, pp. 668-673.
2. S. Nayar, S. Nene, and H. Murase, "Real-time 100 object recognition system," in Proceedings of the IEEE Conference on Robotics and Automation, vol. 3, 1996, pp. 2321-2325.
3. V. Lepetit, L. Vacchetti, D. Thalmann, and P. Fua, "Fully automated and stable registration for augmented reality applications," in Proceedings of the Second IEEE and ACM Symposium on Mixed and Augmented Reality, 2003, pp. 93-102.
4. S. de Backer, A. Naud, and P. Scheunders, "Nonlinear dimensionality reduction techniques for unsupervised feature extraction," Pattern Recognition Letters, vol. 19, no. 8, pp. 711-720, June 1998.
5. R. Souvenir and R. Pless, "Isomap and nonparametric models of image deformation," in IEEE Workshop on Motion and Video Computing, 2005, pp. II: 195-200.
6. P.-J. Wang and C. Cox, "Study on the application of auto-associative neural network," in Proceedings of the Third International Conference on Machine Learning and Cybernetics, vol. 5, 2004, pp. 3291-3295.
7. D. Tzovaras and M. Strintzis, "Use of nonlinear principal component analysis and vector quantization for image coding," IEEE Transactions on Image Processing, vol. 7, no. 8, pp. 1218-1223, August 1998.
8. D. G. Lowe, "Object recognition from local scale-invariant features," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1999, pp. 1150-1157.
9. _, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
10. S.-H. Kim, I.-C. Kim, and I.-S. Kweon, "Probabilistic model-based object recognition using local zernike moments," in IAPR workshop on Machine Vision Applications, Nara, Japan, Dec. 2002.
11. M. Tarr, P. Williams, W. Hayward, and I. Gauthier, "Three- dimensional object recognition is viewpoint dependent," Nature Neuroscience, pp. 275-277, 1998.
12. N. Logothetis, J. Pauls, H. Bülthoff, and T. Poggio, "View-dependent object recognition by monkeys," Current Biology, vol. 4, pp. 401-414, 1994.
13. I. Biedermann, "Recognition-by-components: A theory of human image understanding," Psychological Review, vol. 94, pp. 369-384, 1987.
14. H. Bülthoff and S. Edelmann, "Psychophysical support for a two-dimensional view interpolation theory of object recognition," in Proceedings of the National Academy of Sciences, vol. 89, 1992, pp. 60-64.
15. N. Logothetis, J. Pauls, and T. Poggio, "Shape representation in the inferior temporal cortex of monkeys," Current Biology 5, pp. 552-563, 1995.
16. M. W. Oram and D. I. Perrett, "Modeling visual recognition from neurobiological constraints," Neural Networks, vol. 7, no. 6-7, pp. 945-972, 1994.
17. B. A. Olshausen, C. H. Anderson, and D. C. V. Essen, "A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information," The Journal of Neuroscience, vol. 13, no. 11, pp. 4700-4719, 1993.
18. M. A. Kramer, "Nonlinear principal component analysis using autoassociative neural networks," AICHE Journal, vol. 37, pp. 233-243, 1991.
19. A. N. Kolmogorov, "On the representation, of continuous functions of many variables by superpositions of continuous functions of one variable and addition," Akademii Nauk USSR, vol. 114, no. 5, pp. 953-956, 1957.
20. V. Kurková, "Kolmogorov's theorem, and multilayer neural networks," Neural Networks, vol. 5, no. 3, pp. 501-506, 1992.
21. T. S. Lee, "Image representation using 2d gabor wavelets," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 10, pp. 959-971, 1996.
22. A. Bell and T. Sejnowski, "The independent components of natural scenes are edge filters," in Vision Research, vol. 37, 1997, pp. 3327-3338.
23. L. Wiskott, J. Fellous, N. Krüger, and C. von der Malsburg, "Face recognition by elastic bunch graph matching," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775-779, July 1997.
24. J. M. Geusebroek, G. J. Burghouts, and A. W. M. Smeulders, "The Amsterdam, library of object images," Int. J. Comput. Vision, vol. 61, no. 1, pp. 103-112, 2005.