Hypotheses for image features, icons and textons

International Journal of Computer Vision

Volumn 70, Issue 3, 2006, Pages 213-230

  Griffin, Lewis D ^a   Lillholm, Martin ^b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b IT UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE PROCESSING; SIGNAL FILTERING AND PREDICTION; VECTORS;

FILTER-RESPONSE SPACE; IMAGE FEATURES; QUANTITATIVE DESCRIPTION; TEXTON HYPOTHESIS;

COMPUTER VISION;

EID: 33746438034     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-006-6355-9     Document Type: Conference Paper

References (94)

1. Barlow, H. B. 1953. Summation and inhibition in the frog's retina. Journal of Physiology (London), 119:69-88.
2. Barlow, H. B. 1972. Single units and sensation: a neuron doctrine for perceptual psychology? Perception, 1: 371-394.
3. Berlin, B. and Kay, P. 1969. Basic Color Terms: their Universality and Evolution, Berkeley: University of California Press.
4. Bimler, D. 2004. Personal Communication.
5. Buchsbaum, G. and Bloch, O. 2002. Color categories revealed by non-negative matrix factorization of Munsell color spectra. Vision Research, 42:559-563.
6. Cen, F., et al. 2004. Robust registration of 3-D ultrasound images based on gabor filter and mean-shift method. In Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis, p. 304-316.
7. Davidoff, J., Davies, I., and Roberson, D. 1999. Colour categories in a stone-age tribe. Nature, 398 (6724):203-204.
8. Debnath, L. 1964. On Hermite Transforms. Mathematicki Vesnik, l(16):285-292.
9. Debnath, L. 1995. Integral Transforms and their Applications, CRC Press.
10. DeValois, R. L., Abramov, I., and Jacobs, G. H. 1966. Analysis of response patterns of LGN cells. Journal of the Optical Society of America, 56:966-977.
11. Dowman, M. 2002. Modelling the acquisition of colour words. In Al 2002: Advances in Artificial Intelligence, p. 259-271.
12. Ellison, T. M. 2001. Induction and inherent similarity. In U. Hahn and M. Ramscar (Eds.) Similarity and Categorization, OUP: Oxford, p. 29-49.
13. Florack, L. M. J., et al. 1992. Families of Tuned Scale-Space Kernels. In Computer Vision - ECCV '92, p. 19-23.
14. Gärdenfors, P. 2000. Conceptual Spaces: the geometry of thought, Cambridge MA: MIT Press.
15. Georgeson, M. A. and Freeman, T. C. A. 1997. Perceived location of bars and edges in one-dimensional images: Computational models and human vision. Vision Research, 37(1):127-142.
16. Geusebroek, J. M., et al. 2003. Color constancy from physical principles. Pattern Recognition Letters, 24(11):1653-1662.
17. Gibson, J. J. 1979. The Ecological Approach to Visual Perception, Houghton Mifflin.
18. Griffin, L. D. 1995. Descriptions of Image Structure, London: PhD thesis, University of London.
19. Griffin, L. D. 1997. Critical Points in Affine Scale Space. In Gaussian Scale-Space Theory, S. Sporring, et al. (Ed.) p. 165-180.
20. Griffin, L. D. 2001. Similarity of Pyschological and Physical Colour Space shown by Symmetry Analysis. Color Research and Application, 26(2): 151-157.
21. Griffin, L. D. 2002. Local image structure, metamerism, norms, and natural image statistics. Perception, 31(3):377-377.
22. Griffin, L. D. 2005. Feature classes for 1-D, 2nd order image structure arise from the maximum likelihood statistics of natural images. Network-Computation in Neural Systems, in press.
23. Griffin, L. D. and Colchester, A. C. F. 1995. Superficial and Deep-Structure in Linear Diffusion Scale-Space - Isophotes, Critical-Points and Separatrices. Image and Vision Computing, 13(7):543-557.
24. Griffin, L. D. and Lillholm, M. 2003. Mode Estimation by Pessimistic Scale Space Tracking. In Scale Space '03, Isle of Skye, UK: Springer.
25. Griffin, L. D. and Lillholm, M. 2005. Image features and the 1 -D, 2nd order gaussian derivative jet. In Proc. Scale Space 2005. Springer, p. 26-37.
26. Griffin, L. D. and Lillholm, M. 2005. The multiscale mean shift algorithm for mode estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, submitted.
27. Griffin, L. D., Lillholm, M. and Nielsen, M. 2004. Natural image profiles are most likely to be step edges. Vision Research, 44(4): 407-421.
28. Heiler, M. and Schnorr, C. 2005. Natural image statistics for natural image segmentation. International Journal of Computer Vision, 63(1):5-19.
29. Hering, E. 1920. Outlines of a theory of the light sense, Harvard: Harvard University Press.
30. Hubel, D. H. and Wiesel, T. N. 1968. Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology, 195:215-243.
31. Hurvich, L. M. and Jameson, D. 1957. An opponent-process theory of color vision. Psychological Review, 64:384-404.
32. Jameson, K. A. 2005. Culture and Cognition: what is universal about color experience? Cognition and Culture, in press.
33. Kay, P. 2005. Color categories are not arbitrary. Cross-Cultural Research, 39(1):39-55.
34. Kay, P. and Maffi, L. 1999. Color appearance and the emergence and evolution of basic color lexicons. American Anthropologist, 101:743-760.
35. Kay, P. and McDaniel, C. K. 1978. The linguistic significance of the meanings of the basic color terms. Language, 54: 610-646.
36. Kay, P. and Regier, T. 2003. Resolving the question of color naming universals. Proceedings of the National Academy of Sciences of the United States of America, 100(15):9085-9089.
37. Kimmel, R. and Bruckstein, A. M. 2003. Regularized Laplacian Zero Crossings as Optimal Edge Integrators. International Journal of Computer Vision, 53(3):225-243.
38. Koenderink, J. J. 1984. The Structure of Images. Biological Cybernetics, 50(5):363-370.
39. Koenderink, J. J. 1988. Operational Significance of Receptive-Field Assemblies. Biological Cybernetics, 1 58(3): 163-171.
40. Koenderink, J. J. 1993. What is a feature? Journal of Intelligent Systems, 3(1): 49-82.
41. Koenderink, J. J. 2001. Multiple visual worlds (editorial). Perception, 30:1-7.
42. Koenderink, J. J. and van Doom, A. J. 1987. Representation of Local Geometry in the Visual-System. Biological Cybernetics, 55(6): 367-375.
43. Koenderink, J. J. and van Doom, A. J. 1990. Receptive-Field Families. Biological Cybernetics, 63(4):291-297.
44. Koenderink, J. J. and van Doom, A. J. 1992. Generic Neighborhood Operators. leee Transactions on Pattern Analysis and Machine Intelligence, 14(6):597-605.
45. Koenderink, J. J. and van Doom, A. J. 1992. Receptive Field Assembly Specificity. Journal of Visual Communication and Image Representation, 3(1):1-12.
46. Koenderink, J. J. and van Doom, A. J. 1996. Metamerism in complete sets of image operators. In K. W. Bowyer and N. Ahuja (Eds.). Advances in Image Understanding: A Festschrift for Azriel Rosenfeld, Wiley-IEEE Computer Society Press, p. 113-129.
47. Koenderink, J. J. and van Doom, A. J. 1997. Local Image Operators and Iconic Structure, In G. Sommer and J. J. Koenderink (Eds.). Algebraic Frames for the Perception-Action Cycle, Springer, p. 66-93.
48. Koenderink, J. J. and Van Doom, A. J. 1998. The structure of relief, In Advances in Imaging and Electron Physics, 103:65-150.
49. Koenderink, J. J. and van Doom, A. J. 2003. Perspectives on color space. In R. Mausfield and D. Heyer (Eds.). Colour Perception: Mind and the. Physical World, OUP: Oxford, p. 1-56.
50. Lawson, S. and Zhu, J. 2000. Image compression using wavelets and JPEG2000: a tutorial. Electronics & Communication Engineering Journal, 14(3):112-121.
51. Lee, A. B., Pedersen, K. S., and Mumford, D. 2003. The nonlinear statistics of high-contrast patches in natural images. International Journal of Computer Vision, 54(1-2):83-103.
52. Leung, T. and Malik, J. 2001. Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision, 43(1):29-44.
53. Lillholm, M., Nielsen, M., and Griffin, L. D. 2003. Feature-based image analysis. International Journal of Computer Vision, 52(2-3):73-95.
54. Liu, X. W. and Wang, D. L. 2002. A spectral histogram model for texton modeling and texture discrimination. Vision Research, 42(23): 2617-2634.
55. Logothetis, N. K., Pauls J., and Poggio, T. 1995. Shape Representation in the Inferior Temporal Cortex of Monkeys. Current Biology, 5(5):552-563.
56. Majthay, A. 1985. Foundations of Catastrophe Theory, London: Pitman Publishing Ltd.
57. Makram-Ebeid, S. and Mory, B. 2003. Scale-space image analysis based on hermite polynomials theory. In L. D. Griffin and M. Lillholm (Eds.). Proc. Conf. on Scale Space Methods in Computer Vision, Springer, p. 57-71.
58. Manmatha, R., Ravela, S., and Chitti, Y. 1998. On computing local and global similarity in images. In Human Vision and Electronic Imaging III, p. 540-551.
59. Marr, D. and Hildreth, E. 1980. Theory of edge detection. Proceedings of the Royal Society Series B, 20: 187-217.
60. Marr, D., 1982, Vision. New York: W H Freeman & co.
61. Martens, J. B. 1997. Local orientation analysis in images by means of the Hermite transform. IEEE Transactions on Image Processing, 6(8):1103-1116.
62. Martin, D. R., Fowlkes, C. C., and Malik, J. 2004. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5):530-549.
63. Nakamura, K., et al. 1994. Visual Response Properties of Single Neurons in the Temporal Pole of Behaving Monkeys. Journal of Neurophysiology, 71(3):1206-1221.
64. Newton, I. 1706. Enumeratio linearum tertii ordinis.
65. Pedersen, K. S. 2003. Statistics of Natural Image Geometry. In Department of Computer Science, Copenhagen: University of Copenhagen.
66. Richards, W. 1979. Quantifying Sensory Channels - Generalizing Colorimetry to Orientation and Texture, Touch, and Tones. Sensory Processes, 3(3):207-229.
67. Rissanen, J. 1978. Modeling by shortest data description. Automatica, 14:465-471.
68. Rivero-Moreno, C. J. and Bres, S. 2003. Conditions of similarity between hermite and gabor filters as models of the human visual system. In N. Petkov and M. A. Westenberg (Eds.). Computer Analysis of Images and Patterns, Springer-Verlag, Berlin, p. 762-769.
69. Roberson, D. 2005. Color categories are culturally diverse in cognition as well as in language. Cross-Cultural Research,39(1):56-71.
70. Scale Space '01. 2001. In Scale Space '01. Vancouver, Canada: Springer.
71. Scale Space '03.2003. In Scale Space '03. Isle of Skye, UK: Springer.
72. Scale Space '05. 2005. In Scale Space '05. Hofgeismar, Germany: Springer.
73. Scale Space '99. 1999. In Scale Space '99, Corfu, Greece: Springer.
74. Sigala, N. and Logothetis, N. K. 2002. Visual categorization shapes feature selectivity in the primate temporal cortex. Nature, 415 (6869):318-320.
75. Steels, L. and Belpaeme, T. 2005. Coordinating perceptually grounded categories through language. A case study for colour. Behavioral and Brain Sciences, In Press.
76. Tagliati, E. and Griffin, L. D. 2001. Features in Scale Space: Progress on the 2D 2nd Order Jet. In M. Kerckhove (Ed.). LNCS, Springer, p. 51-62.
77. ter Haar Romeny, B. M. 2003. Front-end Vision and Multi-Scale Image Analysis. Kluwer.
78. ter Haar Romeny, B. M. and Florack, L. M. J. 1994. Higher-order differential structure of images. Image, and Vision Computing, 12(6): 317-325.
79. Thom, R. 1972. Structural stability and morphogenesis. Reading MA: W. A. Benjamin, Inc.
80. van den Boomgaard, R. 2003. Least squares and robust estimation of local image structure. In L. D. Griffin and M. Lillholm (Eds.). Proc. Scale Space Methods in Computer Vision, p. 237-254.
81. van Hateren, J. H. and van der Schaaf, A. 1998. Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings of the Royal Society of London Series B-Biological Sciences, 265(1394): 359-366.
82. van Trigt, C. 1990a. Smoothest Reflectance Functions .1. Definition and Main Results. Journal of the Optical Society of America a-Optics Image Science and Vision, 7(10):1891-1904.
83. van Trigt, C. 1990b. Smoothest Reflectance Functions .2. Complete Results. Journal of the Optical Society of America a-Optics Image Science and Vision, 7(12):2208-2222.
84. Varma, M. and Zisserman, A. 2002. Classifying images of materials: achieving viewpoint and illumination independence. In ECCV '02, Copenhagen, Springer.
85. Varma, M. and Zisserman, A. 2005. A statistical approach to texture classification from single images. International Journal of Computer Vision, 62(1-2):61-81.
86. Vogels, R., et al., 2001. Inferior temporal neurons show greater sensitivity to nonaccidental than to metric shape differences. Journal of Cognitive Neuroscience, 13 (4): 444-453.
87. Wilson, M. and Debauche, B. A. 1981. Inferotemporal Cortex and Categorical Perception of Visual- Stimuli by Monkeys. Neumpsychologia, 19(1): 29-41.
88. Wu, S. W. and Gersho, A. 1993. Lapped Vector Quantization of Images. Optical Engineering, 32(7): 1489-1495.
89. Yendrikhovskij, S. N. 2001. Computing color categories from statistics of natural images. Journal of Imaging Science and Technology, 45(5):409-417.
90. Young, R. A. 1987. The Gaussian derivative model for spatial vision: I. Retinal mechanisms. Spatial Vision, 2:273-293.
91. Young, R. A. and Lesperance, R. M. 2001. The Gaussian Derivative model for spatial-temporal vision: II. Cortical data. Spatial Vision, 14(3-4):321-389.
92. Young, R. A., Lesperance, R.M., and Meyer, W. W. 2001. The Gaussian Derivative model for spatial-temporal vision: 1. Cortical model. Spatial Vision, 14(3-4):261-319.
93. Zhilkin, P. and Alexander, M. E. 2000. 3D image registration using a fast noniterative algorithm. Magnetic Resonance Imaging, 18(9):1143-1150.
94. Zhu, S.-C., et al. 2005. What are textons? International Journal of Computer Vision, 62(1):121-143.