Deformable prototypes for encoding shape categories in image databases

Pattern Recognition

Volumn 30, Issue 4, 1997, Pages 627-641

  Sclaroff, Stan ^a  

^a BOSTON UNIVERSITY   (United States)

Author keywords

Deformable templates; Energy based shape models; Image database search; Linear combinations; Modal matching; Object recognition; Shape categories

Indexed keywords

COMPUTATIONAL GEOMETRY; DATABASE SYSTEMS; IMAGE SENSORS; INFORMATION RETRIEVAL; INTERACTIVE COMPUTER GRAPHICS; OBJECT RECOGNITION; ONLINE SEARCHING;

DEFORMABLE TEMPLATES; ENERGY BASED SHAPE MODEL; IMAGE DATABASE SEARCH; MODAL MATCHING; SHAPE CATEGORIES;

IMAGE CODING;

EID: 0031108407     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(96)00108-2     Document Type: Article

References (63)

1. I. Biederman, Human image understanding: Recent research and a theory, Comput. Vision Graphics Image Process. 32(1), 29-73 (1995).
2. M. Leyton, Perceptual organization as nested control, Biol. Cybernetics 51, 141-153 (1984).
3. D. Marr and K. Nishihara, Representation and recognition of the spatial organization of three-dimensional shapes, in Proc. Roy. Soc. London Series B, 200 (1978).
4. A. Pentland, Perceptual organization and representation of natural form, Artificial Intell. 28(3), 293-331 (1986).
5. E. Rosch, Cognitive representations of semantic categories, J. Experimental Psychology: General 104, 193-233 (1975).
6. T. Poggio and F. Girosi, A theory of networks for approximation and learning, Technical Report, A.I. Memo No. 1140, Artificial Intelligence Lab. MIT, Cambridge, Massachusetts (July 1989).
7. S. Ullman and R. Basri, Recognition by linear combinations of models, IEEE Trans. Pattern Analysis Mach. Intell. 13(10), 992-1006 (1991).
8. S. Edelman, Representation, similarity, and chorus of prototypes, Minds Machines 5, 45-68 (1995).
9. T. Darrell and A. Pentland, Space-time gestures, in Proc. CVPR, pp. 335-340 (June 1993).
10. S. Sclaroff, Modal matching: A method for describing, comparing, and manipulating digital signals, Ph.D. Thesis, MIT Media Lab (January 1995).
11. S. Sclaroff and A. Pentland, Modal matching for correspondence and recognition, IEEE Trans. Pattern Analysis Mach. Intell. 17(6), 545-561 (1995).
12. S. Sclaroff and A. Pentland, Physically-based combinations of views: Representing rigid and nonrigid motion, in Proc. IEEE Workshop on Nonrigid and Articulate Motion, Austin, Texas, pp. 158-164 (November 1994).
13. Young Won Lim and Sang Uk Lee, On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques, Pattern Recognition 23(9), 935-952 (1990).
14. M. Black, Recursive non-linear estimation of discontinuous flow fields, in Proc. European Conf. Computer Vision (May 1994).
15. T. Darrell and A. P. Pentland, Robust estimation of multiple models using support maps, IEEE Trans. Pattern Analysis Mach. Intell., 17(5), pp. 474-487 (May 1995).
16. W. B. Thompson, Combining motion and constrast for segmentation, IEEE Trans. Pattern Analysis Mach. Intell. 2, 543-549 (1980).
17. J. Y. A. Wang and E. H. Adelson, Layered representation for motion analysis, in Proc. CVPR, pp. 361-366 (1993).
18. J. Weber and J. Malik, Robust computation of optical flow in a multi-scale differential framework, in Proc. Int. Conf. Computer Vision, pp. 12-20 (1993).
19. M. Celenk, A color clustering technique for image segmentation, Comput. Graphics Vision Image Process, 52, 145-170 (1990).
20. G. Healey, Segmenting images using normalized color, IEEE Trans. Systems Man Cybernet. 22, 64-73 (1992).
21. G. J. Klinker, S. A. Shafer and T. Kanade, A physical approach to color image understanding, Int. J. Comput. Vision 4, 7-38 (1990).
22. J. Q. Liu and Y. H. Yang, Multiresolution color image segmentation, IEEE Trans. Pattern Analysis Mach. Intell. 16, 689-700 (1994).
23. Y. Ohta, T. Kanade and T. Sakai, Color information for region segmentation, Comput. Graphics Vision Image Process. 13, 222-241 (1980).
24. Z. Chen and S. Y. Ho, Computer vision for robust 3D aircraft recognition with fast library search, Pattern Recognition 24(5), 375-390 (1991).
25. M. A. Ireton and C. S. Xydeas, Classification of shape for content retrieval of images in a multimedia database, in Proc. Sixth Int. Conf. Digital Processing of Signals in Communications, pp. 111-116, Loughborough, U.K. (September 1990).
26. H. V. Jagadish, A retrieval technique for similar shapes, in Proc. Int. Conf. Management of Data, ACM SIGMOD 91, pp. 208-217, Denver, Colorado (May 1991).
27. R. Mehrotra and W. I. Grosky, Shape matching utilizing indexed hypotheses generation and testing, IEEE Trans. Robotics Automation 5(1), 70-77 (1989).
28. W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic and P. Yanker, The QBIC project: Querying images by content using color, texture, and shape, in Proc. SPIE Conf. Storage and Retrieval of Image and Video Databases 1908, pp. 173-187 (February 1993).
29. B. Scassellati, S. Alexopoulos and M. Flickner, Retrieving images by 2-D shape: Comparison of computation methods with human perceptual judgements, in Proc. SPIE Conf. Storage and Retrieval of Image and Video Databases II, 2185 (February 1994).
30. A. Pentland, R. Picard and S. Sclaroff, Photobook: Tools for content-based manipulation of image databases, Int. J. Comput. Vision 18(3), 233-254 (1996).
31. M. Kass, A. Witkin and D. Terzopoulos, Snakes: Active contour models, Int. J. Comput. Vision 1, 321-331 (1987).
32. D. Terzopoulos, Topical Meeting on Machine Vision, Vol. 12 of Technical Digest Series, chapter On matching deformable models to images: Direct and iterative solutions, pp. 160-167. Optical Society of America, Washington, DC (1987).
33. A. Yuille, D. Cohen, and P. Hallinan, Feature extraction from faces using deformable templates, in Proc. CVPR, pp. 104-109, San Diego (1989) (1987).
34. A. Blake, R. Curwen and A. Zisserman, A framework for spatiotemporal control in the tracking of visual contours, Int. J. Comput. Vision 11(2), 127-146 (1993).
35. F. Bookstein, Principal warps: Thin-plate splines and the decomposition of deformations, IEEE Trans. Pattern Analysis Mach. Intell. 11(6), 567-585 (June 1989).
36. J. Duncan, R. Owen, L. Staib and P. Anandan, Measurement of non-rigid motion using contour shape descriptors, in Proc. CVPR, pp. 318-324 (1991).
37. T. Cootes, D. Cooper, C. Taylor and J. Graham, Trainable method of parametric shape description, Image Vision Comput. 10(5), 289-294 (June 1992).
38. A. Baumberg and D. Hogg, Learning flexible models from image sequences, in Proc. European Conf. on Computer Vision, pp. 299-308, Stockholm, Sweden (May 1994).
39. K. Bathe, Finite-Element Procedures in Engineering Analysis. Prentice-Hall, Englewood Cliffs, New Jersey (1982).
40. A. Pentland and S. Sclaroff, Closed-form solutions for physically-based shape modeling and recognition, IEEE Trans. Pattern Analysis Mach. Intell 13(7), 715-729 (July 1991).
41. C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D. Petkovic and W. Equitz, Efficient and effective querying by image content, J. Intell. Inf. Systems 3, 231-262 (1994).
42. D. Terzopoulos, The computation of visible surface representations, IEEE Trans. Pattern Analysis Mach. Intell. 10(4), 417-438 (July 1988).
43. A. Tversky, Features of similarity, Psych. Rev. 84(4), 327-352 (1977).
44. S. Edelman and D. Weinshall, A self-orgainizing multipleview representation for 3-D objects, Biol. Cybernet. 64, 209-219 (1991).
45. S. Edelman, On learning to recognize 3-D objects from examples, IEEE Trans. Pattern Analysis Mach. Intell. 15(8), 833-837 (1993).
46. E. Oja, Principal components, minor components and linear neural networks, Neural Networks 5, 927-935 (1992).
47. E. Oja and J. Karhunen, Nonlinear PCA: Algorithms and applications, Technical Report A18, Helsinki University of Technology, Laboratory of Computer and Information Sciences, SF-02150 Espoo, Finland (1993).
48. R. O. Duda and P. E. Hart, Pattern Recognition and Scene Analysis. Wiley, New York (1973).
49. K. Fukunaga and W. Koontz, Application of the Karhunen-Loeve expansion to feature selection and ordering, IEEE Trans. Comm. 19(4), 311-318 (1970).
50. D. Huttenlocher, G. Klanderman and W. Rucklidge, Comparing images using the Hausdorff distance, IEEE Trans. Pattern Analysis Mach. Intell. 15(9), 850-863 (September 1993).
51. H. Alt, B. Behrend and J. Blomer, Measuring the resemblance of polygonal shapes, in Proc. Seventh ACM Symp. on Computational Geometry (1991).
52. L. Shapiro and J. Brady, Feature-based correspondence: An eigenvector approach, Image Vision Comput. 10(5), 283-288 (June 1992).
53. I. Greenberg, Guide to Corals and Fishes of Florida, the Bahamas and the Caribbean. Seahawk Press, Miami, Florida (1977).
54. S. Dudani, K. Breeding and R. McGhee, Aircraft identification by moment invariants, IEEE Trans. Comput. 26(1), 39-47 (1977).
55. M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, D. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele and P. Yanker, Query by image and video content: The QBIC system, IEEE Computer, pp. 23-30 (September 1995).
56. A. Gupta, Visual information retrieval technology: A virage perspective, Technical Report Revision 3a, Virage Inc., 177 Bovet Road, Suite 540, San Mateo, California 94403 (1996).
57. M. La Cascia and E. Ardizzone, Jacob: Just a content-based query system for video databases, in Proc. ICASSP, Atlanta (1996).
58. G. Salton and M. J. McGill, Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983).
59. B. Horn, Closed-form solution of absolute orientation using unit quarternions, JOSA-A 4, 629-642 (1987).
60. G. Taubin and D. Cooper, Recognition and positioning of rigid objects, in Proc. SPIE Conf. on Geometric Methods in Computer Vision 1570 (1991).
61. D. Mumford, Mathematical theories of shape: Do they model perception? in Proc. SPIE Conf. on Geometric Methods in Computer Vision 1570 (1991).
62. W. Grosky, P. Neo and R. Mehrotra, A pictorial index mechanism for model-based matching, Data Knowledge Eng. 8, 309-327 (1992).
63. A. Pentland, Automatic extraction of deformable part models, Int. J. Comput. Vision 4(2), 107-126 (March 1990).