The What-and-Where Filter: A Spatial Mapping Neural Network for Object Recognition and Image Understanding

Computer Vision and Image Understanding

Volumn 69, Issue 1, 1998, Pages 1-22

  Carpenter, Gail A ^a   Grossberg, Stephen ^a   Lesher, Gregory W ^a  

^a BOSTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE QUALITY; IMAGE UNDERSTANDING; INTERPOLATION; LEARNING SYSTEMS; MATHEMATICAL TRANSFORMATIONS; NEURAL NETWORKS; OBJECT RECOGNITION;

WHAT AND WHERE FILTERS;

OPTICAL FILTERS;

EID: 0031653659     PISSN: 10773142     EISSN: None     Source Type: Journal    
DOI: 10.1006/cviu.1997.0561     Document Type: Article

References (86)

1. D. Casasent and D. Psaltis, Position, rotation and scale invariant optical correlation, Appl. Opt. 15, 1976, 1795-1799.
2. P. Cavanagh, Image transforms in the visual system, in Figural Synthesis (P. C. Dodwell and T. Caelli, Eds.), pp. 185-218, Erlbaum Associates, Hillsdale, NJ, 1984.
3. A. Mishkin, L. G. Ungerleider, and K. A. Macko, Object vision and spatial vision: Two cortical pathways, Trends in Neurosci. 6, 1983, 414-417.
4. L. G. Ungerleider and M. Mishkin, Two cortical visual systems: Separation of appearance and location of objects, in Analysis of Visual Behavior (D. L. Ingle, M. A. Goodale, and R. J. W. Mansfield, Eds.), pp. 549-586, MIT Press, Cambridge, MA, 1982.
5. M. A. Goodale and D. Milner, Separate visual pathways for perception and action, Trends in Neurosciences 15, 1992, 20-25.
6. S. Grossberg, Neural networks for visual perception in variable illumination, Optics News 8, 1988, 5-10.
7. S. Grossberg and D. Todorović, Neural dynamics of 1-D and 2-D brightness perception: A unified model of classical and recent phenomena, Perception and Psychophysics 43, 1988, 241-277.
8. A. G. Andreou and K. A. Boahken, Modeling inner and outer plexiform retinal processing using nonlinear coupled resistive networks, in Human Vision, Visual Processing, and Digital Display, II. Vol. 1453, pp. 270-281, Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, 1991.
9. S. Grossberg, Cortical dynamics of 3-D form, color, and brightness perception, I. Monocular theory. Perception Psychophys. 41, 1987, 87-116.
10. A. Gove, S. Grossberg, and E. Mingolla, Brightness perception, illusory contours, and corticogeniculate feedback, Visual Neurosci. 12, 1995, 1027-1052.
11. S. Grossberg and E. Mingolla, Neural dynamics of perceptual grouping: Textures, boundaries, and emergent segmentations, Perception and Psychophysics 38, 1985, 141-171.
12. S. Grossberg and E. Mingolla, Neural dynamics of surface perception: Boundary webs, illuminants, and shape-from-shading, Computer Vision, Graphics, and Image Processing 37, 1987, 116-165.
13. S. Grossberg, E. Mingolla, and D. Todorović, A neural network architecture for preattentive vision, IEEE Transactions on Biomedical Engineering 36, 1989, 65-84.
14. S. Grossberg, E. Mingolla, and J. Williamson, Synthetic aperture radar processing by a multiple scale neural system for boundary and surface representation, Neural Networks 8, 1995, 1005-1028.
15. G. A. Carpenter, S. Grossberg, and C. Mehanian, Invariant recognition of cluttered scenes by a self-organizing ART architecture: CORT-X boundary segmentation, Neural Networks 2, 1989, 169-181.
16. S. Grossberg and L. Wyse, A neural network architecture for figure-ground separation of connected scenic figures, Neural Networks 4, 1991, 723-742.
17. S. Grossberg and L. Wyse, Figure-ground separation of connected scenic figures: Boundaries, filling-in, and opponent processing, in Neural Networks for Vision and Image Processing (G. A. Carpenter and S. Grossberg, Eds.), pp. 161-194, MIT Press, Cambridge, MA, 1992.
18. S. Grossberg, A solution of the figure-ground problem for biological vision, Neural Networks 6, 1993, 463-483.
19. S. Grossberg, 3-D vision and figure-ground separation by visual cortex, Perception and Psychophysics 55, 1994, 48-120.
20. S. Grossberg, Cortical dynamics of 3-D figure-ground perception of 2-D pictures, 1997.
21. S. Grossberg and N. P. McLoughlin, Cortical dynamics of 3-D surface perception: Binocular and half-occluded scenic images, Technical Report CAS/CNS-TR-95-022, Boston University, 1996. Neural Networks, in press.
22. G. A. Carpenter and S. Grossberg, A massively parallel architecture for a self-organizing neural pattern recognition machine, Computer Vision, Graphics, and Image Processing 37, 1987, 54-115.
23. G. A. Carpenter and S. Grossberg, ART 2: Stable self-organization of pattern recognition codes for analog input patterns, Applied Optics 26, 1987, 4919-4930.
24. G. A. Carpenter, S. Grossberg, and D. B. Rosen, ART2-A: An adaptive resonance algorithm for rapid category learning and recognition, Neural Networks 4, 1991, 493-504.
25. G. A. Carpenter, S. Grossberg, and D. B. Rosen, Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system, Neural Networks 4, 1991, 759-771.
26. Y. Asfour, G. A. Carpenter, S. Grossberg, and G. W. Lesher, Fusion ARTMAP: A neural network architecture for multi-channel data fusion and classification, Technical Report CAS/CNS-93-006, Boston University, 1993. [In Proceedings of the World Congress on Neural Networks, Portland, II, pp. 210-215, Erlbaum Associates, Hillsdale, NJ]
27. Y. Asfour, G. A. Carpenter, S. Grossberg, and G. W. Lesher, Fusion ARTMAP: A neural network architecture for multi-channel data fusion and classification, Technical Report CAS/CNS-93-006, Boston University, 1993. [In Proceedings of the World Congress on Neural Networks, Portland, II, pp. 210-215, Erlbaum Associates, Hillsdale, NJ]
28. G. A. Carpenter and S. Grossberg, Fuzzy ARTMAP: Supervised learning, recognition, and prediction by a self-organizing neural network, IEEE Communications Magazine, September, 1992, 38-49.
29. G. A. Carpenter, S. Grossberg, and J. H. Reynolds, ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network, Neural Networks 4, 1991, 565-588.
30. G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Transactions on Neural Networks 3, 1992, 698-713.
31. J. R. Williamson, Gaussian ARTMAP: A neural network for fast incremental learning of noisy multidimensional maps, Neural Networks, 1996.
32. G. A. Carpenter and W. D. Ross, ART-EMAP: A neural network architecture for learning and prediction by evidence accumulation, in Proceedings of the World Congress on Neural Networks, Portland, III, pp. 649-656, Erlbaum Associates, Hillsdale, NJ, 1993.
33. G. A. Carpenter and W. D. Ross, ART-EMAP: A neural network architecture for object recognition by evidence accumulation, IEEE Transactions on Neural Networks 6, 1995, 805-818.
34. G. Bradski and S. Grossberg, A neural architecture for 3-D object recognition from multiple 2-D views, in Proceedings of the World Congress on Neural Networks, San Diego, IV, pp. 211-219, Erlbaum Associates, Hillsdale, NJ, 1994.
35. G. Bradski and S. Grossberg, Fast learning VIEWNET architectures for recognizing 3-D objects from multiple 2-D views, Neural Networks 8, 1995, 1053-1080.
36. S. Grossberg, E. Mingolla, and W. Ross, A neural theory of attentive visual search: Interactions of boundary, surface, spatial, and object representations. Psychological Review 101, 1994, 470-489.
37. Y. Yeshurun and E. L. Schwartz, Shape description with a space-variant sensor: Algorithms for scan-path, fusion and convergence over multiple scans, IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 1989, 1217-1222.
38. P. M. Daniel and D. Whitteridge, The representation of the visual field on the cerebral cortex in monkeys, Journal of Physiology 159, 1961, 203-221.
39. B. Fischer, Overlap of receptive field centers and representation of the visual field in the cat's optic tract, Vision Research 13, 1973, 2113-2120.
40. E. L. Schwartz, Spatial mapping and spatial vision in primate striate and inferotemporal cortex, in Sensory Experience, Adaptation, and Perception (L. Spillmann and B. R. Woolen, Eds.), pp. 73-104, Erlbaum Associates, Hillsdale, NJ, 1984.
41. R. B. H. Tootell, M. S. Silverman, E. Switkes, and R. L. DeValois, Deoxyglucose analysis of retinotopic organization in primate striate cortex. Science 218, 1982, 902-904.
42. D. C. van Essen, W. T. Newsome, and J. H. R. Maunsell, The visual representation in striate cortex of macaque monkey: Asymmetries, anisotropies, and individual variability, Vision Research 24, 1984, 429-448.
43. S. Grossberg, Contour enhancement, short-term memory and constancies in reverberating neural networks, Stud. Appl. Math. 52, 1973, 217-257. [Reprinted in S. Grossberg, Studies of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition, and Motor Control. Boston: Reidel Press, 1982]
44. S. Grossberg, Contour enhancement, short-term memory and constancies in reverberating neural networks, Stud. Appl. Math. 52, 1973, 217-257. [Reprinted in S. Grossberg, Studies of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition, and Motor Control. Boston: Reidel Press, 1982]
45. J. Feldman and D. Ballard, Connectionist models and their properties. Cognitive Science 6, 1982, 205-254.
46. K. P. Hadeler, On the theory of lateral inhibition, Kybernetik 14, 1974, 161-165.
47. C. Koch and S. Ullman, Shifts in selective visual attention: Towards the under-laying neural circuitry, Human Neurobiology 4, 1985, 219-227.
48. J. K. Tsotsos, S. M. Culhane, W. Y. K. Wai, Y. Lai, N. Davis, and F. Nuflo, Modeling visual attention via selective tuning, Artificial Intelligence, 1995, in press.
49. D. H. Hubel and T. N. Wiesel, Functional architecture of macaque monkey visual cortex, Proceedings of the Royal Society of London (B) 198, 1977, 1-59.
50. M. Seibert and A. M. Waxman, Spreading activation layers, visual saccades, and invariant representations for neural pattern recognition systems, Neural Networks 2, 1989, 9-27.
51. S. Grossberg, A theory of human memory: Self-organization and performance of sensory-motor codes, maps, and plans, in Progress in Theoretical Biology, R. Rosen and F. Snell (Eds.), Vol. 5, Academic Press, New York, 1978.
52. S. Grossberg and M. Kuperstein, Neural Dynamics of Adaptive Sensory Motor Control: Expanded Edition, Elsevier Science, Tarrytown, NY, 1986/1989.
53. M. A. Cohen and S. Grossberg, Absolute stability of global pattern formation and parallel memory storage by competitive neural networks, IEEE Transactions on Systems Man, and Cybernetics SMC-13, 1983, 815-826.
54. M. Cohen and S. Grossberg, Neural dynamics of speech and language coding: Developmental programs, perceptual grouping, and competition for short term memory, Human Neurobiology 5, 1986, 1-22.
55. R. Coultrip, R. Granger, and G. Lynch, A cortical model of winner-take-all competition via lateral inhibition, Neural Networks 5, 1992, 47-54.
56. S. A. Ellias and S. Grossberg, Pattern formation, contrast control, and oscillations in the short term memory of shunting on-center off-surround networks, Biological Cybernetics 20, 1975, 69-98.
57. B. Ermentrout, Complex dynamics in winner-take-all neural nets with slow inhibition, Neural Networks 5, 1992, 415-431.
58. S. Grossberg, Competition, decision, and consensus, Journal of Mathematical Analysis and Applications 66, 1978, 470-493.
59. S. Grossberg and D. Levine, Some developmental and attentional biases in the contrast enhancement and short term memory of recurrent neural networks, Journal of Theoretical Biology 53, 1975, 341-380.
60. G. A. Carpenter and S. Grossberg (Eds.), Pattern Recognition by Self-Organizing Neural Networks, MIT Press, Cambridge, MA, 1991.
61. G. A. Carpenter and S. Grossberg, Normal and amnesic learning, recognition, and memory by a neural model of cortico-hippocampal interactions, Trends in Neurosciences 16, 1993, 131-137.
62. R. Desimone, Neural circuits for visual attention in the primate brain, in Neural Networks for Vision and Image Processing (G. A. Carpenter and S. Grossberg, Eds.), pp. 343-364, MIT Press, Cambridge, MA, 1992.
63. E. Miller, L. Li, and R. Desimone, Activity of neurons in anterior inferior temporal cortex during a short-term memory task, Journal of Neuroscience 13, 1993, 1460-1478.
64. B. Motter, Neural correlates of attentive selection of color or luminance in extrastriate area V4, Journal of Neuroscience 14, 1994, 2178-2189.
65. J. Reynolds, L. Chelazzi, S. Luck, and R. Desimone, Sensory interactions of selective spatial attentive in macaque area V2, Society for Neuroscience Abstracts 20, 1994, 1054.
66. J. Reynolds, J. Nicholas, L. Chelazzi, and R. Desimone, Spatial attention protects macaque V2 and V4 cells from the influence of non-attended stimuli, Society for Neuroscience Abstracts 21, 1995, 356.
67. S. Grossberg, The attentive brain, American Scientist 83, 1995, 438-449.
68. M. Bravo and R. Blake, Preattentive vision and perceptual groups, Perception 19, 1990, 515-522.
69. A. Cohen and R. B. Ivry, Density effects in conjunctive search: Evidence for a coarse location mechanism of feature integration, Journal of Experimental Psychology: Human Perception and Performance 17, 1991, 891-901.
70. J. T. Enns and R. A. Rensink, Influence of scene-based properties on visual search, Science 247, 1990, 721-723.
71. Z. J. He and K. Nakayama, Surface features in visual search, Nature 359, 1992, 231-233.
72. J. T. Mordkoff, S. Yantis, and H. Egeth, Detecting conjunctions of color and form in parallel, Perception and Psychophysics 5, 1990, 157-168.
73. J. Wolfe, K. R. Cave, and S. L. Franzel, Guided search: An alternative to the feature integration model of visual search, Journal of Experimental Psychology: Human Perception and Performance 15, 1989, 419-433.
74. J. M. Wolfe and S. R. Friedman-Hill, Part-whole relationships in visual search, Investigative Ophthalmology and Visual Science 33, 1992, 1355.
75. B. A. Olshausen, C. H. Anderson, and D. C. van Essen, A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information, Journal of Neuroscience 13, 1993, 4700-4719.
76. J. J. Hopfield, Neurons with graded response have collective computational properties like those of two-state neurons, Proceedings of the National Academy of Sciences 81, 1984, 3058-3092.
77. G. A. Carpenter and S. Grossberg, Integrating symbolic and neural processing in a self-organizing architecture for pattern recognition and prediction, in Artificial Intelligence and Neural Networks: Steps towards Principled Predictions (V. Honavar and L. Uhr, Eds.), pp. 387-421, Academic Press, San Diego, 1994.
78. R. Remington and L. Pierce, Moving attention: Evidence for time-invariant shifts of visual selective attention, Perception and Psychophysics 35, 1984, 393-399.
79. B. Kröse and B. Julesz, The control and speed of shifts of attention, Vision Research 29, 1989, 1607-1619.
80. S. Grossberg and M. Rudd, A neural architecture for visual motion perception: Group and element apparent motion, Neural Networks 2, 1989, 421-450.
81. S. Grossberg and M. E. Rudd, Cortical dynamics of visual motion perception: Short-range and long-range apparent motion, Psychological Review 99, 1992, 78-121.
82. G. Francis and S. Grossberg, Cortical dynamics of form and motion integration: Persistence, apparent motion, and illusory contours, Vision Research 36, 1996, 149-173.
83. P. A. Kolers, Aspects of Motion Perception, Pergamon Press, Oxford, 1972.
84. S. Grossberg, Why do parallel cortical systems exist for the perception of static form and moving form? Perception and Psychophysics 49, 1991, 117-141.
85. S. Grossberg and E. Mingolla, Visual motion perception, in Encyclopedia of Human Behavior, Vol. 4, (V. S. Ramachandran, Ed.), pp. 469-486. Academic Press, New York, 1994.
86. W. T. Freeman and E. H. Adelson, Steerable filters, IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 1991, 891-906.