Learning and vision machines

Proceedings of the IEEE

Volumn 90, Issue 7, 2002, Pages 1164-1176

  Heisele, Bernd ^a,b,e,f   Verri, Alessandro ^a,c,g   Poggio, Tomaso ^{a,d,h,i,j,k,l}  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b Honda R&D Americas Inc   (United States)

^c UNIVERSITY OF GENOA   (Italy)

^d MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^e Honda R and D America   (United States)

^f Honda Research Laboratory   (United States)

^g Department of Computer and Information Science ^*  (Italy)

^h MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

ⁱ MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^j American Association for Artificial Intelligence   (United States)

^k AMERICAN ACADEMY OF ARTS AND SCIENCES   (United States)

^l Kockfeller University   (United States)

more..

Author keywords

Morphable models; Object categorization; Object detection; Object recognition; Pattern classification; Support vector machines; Visual learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; INTELLIGENT CONTROL; LEARNING SYSTEMS; OBJECT RECOGNITION; PATTERN RECOGNITION; PROBLEM SOLVING; STATISTICAL METHODS;

MORPHABLE MODELS; OBJECT CATEGORIZATION; OBJECT DETECTION; SUPPORT VECTOR MACHINES; VISUAL LEARNING;

COMPUTER VISION;

EID: 1642427649     PISSN: 00189219     EISSN: None     Source Type: Journal    
DOI: 10.1109/JPROC.2002.801450     Document Type: Review

References (65)

1. T. Poggio and S. Edelman, "Network that learns to recognize 3-D objects," Nature, vol. 343, pp. 263-266, 1990.
2. H. H. Bülthoff and S. Edelman, "Psychophysical support for a 2-D view interpolation theory of object recognition," in Proc. Nat. Acad. Sci., vol. 89, 1992, pp. 60-64.
3. N. Logothetis, J. Pauls, and T. Poggio, "Shape representation in the inferior temporal cortex of monkeys," Current Biol., vol. 5, pp. 552-563, 1995.
4. V.N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
5. T. Evgeniou, M. Pontil, and T. Poggio, "Regularization networks and support vector machines," Adv. Computat. Math., vol. 13, pp. 1-50, 2000.
6. F. Cucker and S. Smale, "On the mathematical foundations of learning," Bull. Amer. Math. Soc., vol. 39, no. 1, pp. 1-49, 2002.
7. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 1995.
8. A. N. Tikhonov and V. Y. Arsenin, Solutions of Ill-Posed Problems. Washington, DC: Winston, 1977.
9. C. Papageorgiou, M. Oren, and T. Poggio, "A general framework for object detection," in Proc. Int. Conf. Computer Vision, Bombay, India, 1998, pp. 555-562.
10. V. N. Vapnik and A. Y. Chervonenkis, "On the uniform convergence of relative frequencies of events to their probabilities," Theory Probab. Appl., vol. 17, pp. 264-280, 1971.
11. M. Kearns and R. Shapire, "Efficient distribution-free learning of probabilistic concepts," J. Comput. Syst. Sci., vol. 48, pp. 464-497, 1994.
12. N. Alon, S. Ben-David, N. Cesa-Bianchi, and D. Haussler, "Scale-sensitive dimensions, uniform convergence, and learnability," in Synp. Foundations of Computer Science, 1993.
13. L. Devroye, L. Györfi, and G. Lugosi. A Probabilistic Theory of Pattern Recognition. New York: Springer, 1996.
14. G. Wahba, Splines Models for Observational Data, ser. Series in Applied Mathematics. Philadelphia, PA: SIAM, 1990, vol. 59.
15. F. Girosi, M. Jones, and T. Poggio, "Regularization theory and neural networks architectures," Neural Computat., vol. 7, pp. 219-269, 1995.
16. C. Cortes and V. Vapnik, "Support vector networks," Mach. Learning, vol. 20, pp. 1-25, 1995.
17. E. Osuna, R. Freund, and F. Girosi, "An improved training algorithm for support vector machines," in Proc. IEEE Workshop on Neural Networks and Signal Processing, Amelia Island, FL, 1997, pp. 276-285.
18. J. C. Platt, "Sequential minimal imization: A fast algorithm for training support vector machines," Microsoft Research, Tech. Rep. MST-TR-98-14, Apr. 1998.
19. T. K. Leung, M. C. Burl, and P. Perona, "Finding faces in cluttered scenes using random labeled graph matching," in Proc. Int. Conf. Computer Vision, Cambridge, MA, 1995, pp. 637-644.
20. L. Wiskott, J.-M. Fellous, N. Krüger, and C. von der Malsburg, "Face recognition by elastic bunch graph matching," Proc. IEEE 7th Int. Conf. Comput. Analysis of Images and Patterns (CAIP'97), pp. 456-463, 1997.
21. H. A. Rowley, S. Baluja, and T. Kanade, "Neural network-based face detection," IEEE Trans. Pattern Anal. Machine Intell., vol. 20, pp. 23-38, 19987.
22. T. D. Rikert, M. J. Jones, and P. Viola, "A cluster-based statistical model for object detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1999.
23. H. Schneiderman and T. Kanade, "A statistical method for 3-D object detection applied to faces and cars," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 746-751, 2000.
24. C. Papageorgiou and T. Poggio, "A trainable system for object detection," Int. J. Comput. Vis., vol. 38, pp. 15-33, 2000.
25. B. Heisele, T. Serre, M. Pontil, and T. Poggio, "Component-based face detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 657-662, 2001.
26. A. Mohan, C. Papageorgiou, and T. Poggio, "Example-based object detection in images by components," IEEE Trans. Pattern Anal. Machine Intell., vol. 23, pp. 349-361, 2001.
27. V. Blanz and T. Vetter, "A morphable model for the synthesis of 3-D faces," in Proc. SIGGRAPH, 1999, pp. 187-194.
28. H. A. Rowley, S. Baluja, and T. Kanade, "Rotation Invariant Neural Network-Based Face Detection,", Pittsburgh, PA, Computer Science Tech. Rep. CMU-CS-97-201, 1997.
29. B. Schölkopf, C. Burges, and V. Vapnik, "Extracting support data for a given task," presented at the 1st Int. Conf. Knowledge Discovery and Data Mining, Menlo Park, CA, 1995.
30. M. Pontil and A. Verri, "Support vector machines for 3D object recognition," IEEE Trans. Pattern Anal. Machine Intell., vol. 20, pp. 637-646, 1998.
31. G. Guodong, S. Li, and C. Kapluk, "Face recognition by support vector machines," Proc. IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 196-201, 2000.
32. J. Platt, N. Cristianini, and J. Shawe-Taylor, "Large margin dags for multiclass classification," in Advances in Neural Information Processing Systems, vol. 12, 2000.
33. C. Nakajima, M. Pontil, B. Heisele, and T. Poggio, "People recognition in image sequences by supervised learning," MIT, AI Memo 1688, 2000.
34. H. Murase and S. K. Nayar, "Visual learning and recognition of 3-D object from appearance," Int. J. Comput. Vis., vol. 14, pp. 5-24, 1995.
35. R. Brunelli and T. Poggio, "Face recognition: Features versus templates," IEEE Trans. Pattern Anal. Machine Intell., vol. 15, pp. 1042-1052, 1993.
36. D. J. Beymer, "Face recognition under varying pose," MIT, AI Memo 1461, 1993.
37. L. Sirovitch and M. Kirby, "Low-dimensional procedure for the characterization of human faces," J. Opt. Soc. Amer. A, vol. 2, pp. 519-524, 1987.
38. M. Turk and A. Pentland, "Face recognition using eigenfaces," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 586-591, 1991.
39. P. Belhumeur, P. Hespanha, and D. Kriegman, "Eigenfaces vs fisherfaces: Recognition using class specific linear projection," IEEE Trans. Pattern Anal. Machine Intell., vol. 19, pp. 711-720, 1997.
40. A. V. Nefian and M. H. Hayes, "An embedded HMM-based approach for face detection and recognition," Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, pp. 19-24, 1999.
41. L. Wiskott, J.-M. Fellous, N. Krüger, and C. von der Malsburg, "Face recognition by elastic bunch graph matching," IEEE Trans. Pattern Anal. Machine Intell., vol. 19, pp. 775-779, 1997.
42. K. Jonsson, J. Matas, J. Kittler, and Y. Li, "Learning support vectors for face verification and recognition," Proc. IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 208-213, 2000.
43. B. Heisele, P. Ho, and T. Poggio, "Face recognition with support vector machines: Global versus component-based approach," in Proc. 8th Int. Conf. Computer Vision, Vancouver, BC, Canada, 2001, pp. 688-694.
44. M. Jones and T. Poggio, "Multidimensional morphable models: A framework for representing and matching object classes," in Proc. 6th Int. Conf. Computer Vision, Bombay, India, 1998, pp. 683-688.
45. V. Kumar and T. Poggio, "Learning Based Approach to Estimation of morphable model parameters," MIT, AI Memo 1696, 2000.
46. _, "Learning based approach to real time tracking and analysis of faces," presented at the Int. Conf. Automatic Face and Gesture Recognition, Grenoble, France, 2000.
47. T. F. Cootes, G. J. Edwards, and C. J. Taylor, "Active appearance models," in Proc. 5th Eur. Conf. Computer Vision, 1998, pp. 484-498.
48. T. Ezzat and T. Poggio, "Visual speech synthesis by morphyng visemes," Int. J. Comput. Vis., vol. 38, pp. 45-57, 2000.
49. T. Poggio and T. Vetter, "Recognition and structure from one 2-D model view: Observations on prototypes, object classes and symmetries," MIT, AI Memo 1347, 1992.
50. T. Poggio and R. Brunelli, "A novel approach to graphics," MIT, AI Memo 1354, 1992.
51. D. Beymer, A. Shashua, and T. Poggio, "Example-based image analysis and synthesis," MIT, AI Memo 1431, 1993.
52. D. Beymer and T. Poggio, "Image representation for visual learning," Science, vol. 272, pp. 1905-1909, 1996.
53. M. Jones, P. Sinha, T. Vetter, and T. Poggio, 'Top-down learning of low-level vision tasks," Current Biol., vol. 7, pp. 991-994, 1997.
54. T. Vetter, M. Jones, and T. Poggio, "A bootstrapping algorithm for learning linear models of object classes," in IEEE Conf. Computer Vision and Pattern Recognition, 1997, pp. 40-46.
55. T. Vetter and T. Poggio, "Linear object classes and image synthesis from a single example image," IEEE Trans. Pattern Anal. Machine Intell., vol. 19, pp. 733-742, 1997.
56. S. Librande, Example-Based Character Drawing, Cambridge: M.S., Media Arts and Science Section, School of Architecture and Planning, Massachusetts Inst. Technol., 1992.
57. T. Ezzat and T. Poggio, "MikeTalk: A talking facial display based on morphing visemes," in Proc. Computer Animation Conf., Philadelphia, PA, June 1998, pp. 96-102.
58. M. Pittore, M. Campani, and A. Verri, "Learning to recognize visual dynamic events from examples," Int. J. Comput. Vis., vol. 38, pp. 35-44, 2000.
59. C. Bregler, "Learning and recognizing human dynamics in video sequences," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 568-574, 1997.
60. C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, "Pfinder: Real-time tracking of the human body," IEEE Trans. Pattern Anal. Machine Intell., vol. 19, pp. 780-785, 1997.
61. 4S: A real time system for detecting and tracking people in 2.5 D," presented at the Proc. Eur. Conf. Computer Vision, Friburg, Germany, 1998, pp. 222-227.
62. A. Bobick, S. Intille, J. Davis, F. Baird, C. Pinhanez, L. Campbell, Y. Ivanov, A. Schütte, and A. Wilson, "The KidsRoom: A perceptually-based interactive and immersive story environment," PRESENCE: Teleoperators and Virtual Environments, vol. 8, pp. 367-391, 1999.
63. S. Intille, J. Davis, and A. Bobick, "Real-time closed-world tracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 697-703, 1997.
64. M. A. Giese and T. Poggio, "Morphable models for the analysis and synthesis of complex motion patterns," Int. J. Comput. Vis., vol. 38, pp. 59-73, 2000.
65. C. Papageorgiou, private communication.