A Self-Organizing Neural Network Architecture for Navigation Using Optic Flow

Neural Computation

Volumn 10, Issue 2, 1998, Pages 313-352

  Cameron, Seth ^a   Grossberg, Stephen ^a   Guenther, Frank H ^a  

^a BOSTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; DEPTH PERCEPTION; EYE MOVEMENT; HUMAN; MOVEMENT PERCEPTION; PHYSIOLOGY; REVIEW; SACCADIC EYE MOVEMENT;

COMPUTER SIMULATION; DEPTH PERCEPTION; EYE MOVEMENTS; HUMANS; MOTION PERCEPTION; NEURAL NETWORKS (COMPUTER); SACCADES;

EID: 0032519074     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976698300017782     Document Type: Article

References (63)

1. Banks, M. S., Ehrlich, S. M., Backus, B. T., & Crowell, J. A. (1996). Estimating heading during real and simulated eye movements. Vision Research, 36, 431-443.
2. Bradley, D. C., Maxwell, M., Andersen, R. A., Banks, M. S., & Shenoy, K. V. (1996). Mechanisms of heading perception in primate visual cortex. Science, 273, 1544-1547.
3. Brenner, E. (1991). Judging object motion during smooth pursuit eye movements: The role of optic flow. Vision Research, 31, 1893-1902.
4. Bridle, J. S. (1989). Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In F. Fougelman-Soulie & J. Herrault (Eds.), Neuro-computing: Algorithms, architectures, and applications. Berlin: Springer-Verlag.
5. Bullock, D., & Grossberg, S. (1988). Neural dynamics of planned arm movements: Emergent invariants and speed-accuracy properties during trajectory formation. Psychological Review, 95, 49-90.
6. Chey, J., Grossberg, S., & Mingolla, E. (1996). Neural dynamics of motion grouping: From aperture ambiguity to object speed and direction. Journal of the Optical Society of America A (14), 2570-2594.
7. Cornilleau-Peres, V., & Droulez, J. (1993). Stereo-motion cooperation and the use of motion disparity in the visual perception of 3-D structure. Perception and Psychophysics, 54, 223-239.
8. Crowell, J., & Banks, M. (1993). Perceiving heading with different retinal regions and types of optic flow. Perception and Psychophysics, 53(3), 325-337.
9. Cutting, J. E. (1986). Perception with an eye towards motion. Cambridge, MA: MIT Press.
10. Dron, L. (1993). Dynamic camera self-calibration from controlled motion sequences. In Proceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 501-506). Los Alamitos, CA: IEEE Computer Society Press.
11. Gaudiano, P., & Grossberg, S. (1991). Vector associative maps: Unsupervised real-time error-based learning and control of movement trajectories. Neural Networks, 4, 147-183.
12. Gaudiano, P., Zalama, E., & Lopez-Coronado, J. (1996). An unsupervised neural network for low-level control of a wheeled mobile robot. IEEE Transactions on Systems, Man, and Cybernetics, 26, 485-496.
13. Gibson, J. J. (1950). The perception of the visual world. Boston: Houghton Mifflin.
14. Graziano, M. S. A., Andersen, R. A., & Snowden, R. (1994). Tuning of MST neurons in macaque MST. Society for Neuroscience Abstracts 16, 7.
15. Grossberg, S. (1976). Adaptive pattern classification and universal recoding. I: Parallel development and coding of neural feature detectors. Biological Cybernetics, 23, 121-134.
16. Grossberg, S., & Kuperstein, M. (1989). Neural dynamics of adaptive sensory-motor control (Exp. ed.). Elmsford, NY: Pergamon Press.
17. Hatsopoulos, N. G., & Warren, W. H., Jr. (1991). Visual navigation with a neural network. Neural Networks, 4, 303-317.
18. Heeger, D. J., & Jepson, A. (1990). Visual perception of three-dimensional motion. Neural Computation, 2, 129-137.
19. Heikkonen, J., & Oja, E. (1993). Self-organizing maps for visually guided collision-free navigation. In Proceedings of 1993 International Joint Conference on Neural Networks (Vol. 1, pp. 669-672). New York: IEEE.
20. Hildreth, E. C. (1992). Recovering heading for visually-guided navigation. Vision Research, 32, 1177-1192.
21. Hildreth, E. C., Grzywacz, N. M., Adelson, E. H., & Inada, V. K. (1990). The perceptual buildup of three-dimensional structure from motion. Perception and Psychophysics, 48, 19-36.
22. Holt, R. J., & Netravali, A. N. (1991). Camera calibration problem: Some new results. CVGIP: Image Understanding, 54, 368-383.
23. Koenderink, J. J., & van Doom, A. J. (1975). Invariant properties of the motion parallax field due to the movement of rigid bodies relative to an observer. Optica Acta, 22, 773-791.
24. Koenderink, J. J., & van Doom, A. J. (1987). Facts on optic flow. Biological Cybernetics, 56, 247-254.
25. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59-69.
26. Lagae, L., Maes, H., Raiguel, S., Xiao, D.-K., & Orban, G. A. (1994). Responses of macaque STS neurons to optic flow components: A comparison of areas MT and MST. Journal of Neurophysiology, 71, 1597-1626.
27. Lappe, M., & Rauschecker, J. P. (1993). A neural network for the processing of optic flow from ego-motion in man and higher mammals. Neural Computation, 5, 374-391.
28. Lappe, M., & Rauschecker, J. P. (1995). Motion anisotropies and heading detection. Biological Cybernetics, 72, 261-277.
29. Longuet-Higgins, H. C. (1981). A computer algorithm for reconstructing a scene from two projections. Nature, 293, 133-135.
30. Longuet-Higgins, H. C., & Prazdny, K. (1981). The interpretation of moving retinal images. Proceedings of the Royal Society of London B, 208, 385-397.
31. Luong, Q.-T., & Faugeras, O. D. (1992). Self-calibration of a camera using multiple images. In Proceedings of the 11th IAPR International Conference on Pattern Recognition (Vol. 1, pp. 9-12). Los Alamitos, CA: IEEE Computer Society Press.
32. Maunsell, J. H. R., & Van Essen, D. C. (1983). The connections of the middle temporal visual area (MT) and their relationship to a cortical hierarchy in the macaque monkey. Journal of Neuroscience, 3, 2563-2586.
33. Monen, J., & Brenner, E. (1994). Detecting changes in one's own velocity from the optic flow. Perception, 23, 681-690.
34. Orban, G. A., Lagae, L., Raiguel, S., Xiao, D.-K., & Maes, H. (1995). The speed-tuning of medial superior temporal (MST) cell responses to optic-flow components. Perception, 24, 269-285.
35. Perrone, J. A. (1992). Model for the computation of self-motion in biological systems. Journal of the Optical Society of America A, 9, 1-18.
36. Prazdny, K. (1980). Egomotion and relative depth map from optical flow. Biological Cybernetics, 36, 87-102.
37. Regan, D., & Beverly, K. I. (1982). How do we avoid confounding the direction we are looking and the direction we are moving? Science, 215, 194-196.
38. Rieger, J. H., & Lawton, D. T. (1985). Processing differential image motion. Journal of the Optical Society of America A, 2, 354-359.
39. Rieger, J. H., & Toet, L. (1985). Human visual navigation in the presence of 3D rotations. Biological Cybernetics, 52, 377-381.
40. Rodman, H. R., & Albright, T. D. (1987). Coding of visual stimulus velocity in area MT of the Macaque. Vision Research, 27, 2035-2048.
41. Royden, C. S. (1994). Analysis of misperceived observer motion during simulated eye rotations. Vision Research, 34, 3215-3222.
42. Royden, C. S., Banks, M. S., & Crowell, J. A. (1992). The perception of heading during eye movements. Nature, 360, 583-585.
43. Royden, C. S., Crowell, J. A., & Banks, M. S. (1994). Estimating heading during eye movements. Vision Research, 34, 3197-3214.
44. Simpson, W. A. (1988). Depth discrimination from optic flow. Perception, 17, 497-512.
45. Simpson, W. A. (1993). Optic flow and depth perception. Spatial Vision, 7, 35-75.
46. Tal, D., & Schwartz, E. (1994). Weber-Fechner transduction: A logarithmic compressive nonlinearity is a generic property of integrate and fire neurons. In Proceedings of the World Congress on Neural Networks (Vol. 4, pp. 360-367). Hillsdale, NJ: Erlbaum.
47. Tanaka, K., & Saito, H. (1989). Analysis of motion of the visual field by direction, expansion/contraction, and rotation cells clustered in the dorsal part of the medial superior temporal area of the macaque monkey. Journal of Neurophysiology, 62, 626-641.
48. Treue, S., Andersen, R. A., Ando, H., & Hildreth, E. C. (1995). Structure-from-motion: Perceptual evidence for surface interpolation. Vision Research, 35, 139-148.
49. Ungerleider, L. G., & Desimone, R. (1986). Cortical connections of visual area MT in the Macaque. Journal of Comparative Neurology, 248, 190-222.
50. van den Berg, A. V. (1992). Robustness of perception of heading from optic flow. Vision Research, 32, 1285-1296.
51. van den Berg, A. V. (1993). Perception of heading. Nature, 365, 497-498.
52. von der Malsburg, C. (1973). Self-organization of orientation sensitive cells in the striata cortex. Kybernetik, 14, 85-100.
53. Wallach, H. (1976). On perception. New York: Quadrangle.
54. Wang, H. T., Mathur, B. P., & Koch, C. (1989). Computing optical flow in the primate visual system. Neural Computation, 1, 92-103.
55. Warren, W. H., Jr. (1995). Self-motion: Visual perception and visual control. In W. Epstein & S. Rogers (Eds.), Handbook of perception and cognition: Perception of space and motion (pp. 263-325). San Diego: Academic Press.
56. Warren, W. H., Jr. (1996). The state of flow. In T. Watanabe (Ed.), High level motion processing. Cambridge, MA: MIT Press.
57. Warren, W. H., Jr., & Hannon, D. J. (1988). Direction of self-motion is perceived from optical flow. Nature (London), 336, 162-163.
58. Warren, W. H., Jr., & Hannon, D. J. (1990). Eye movements and optical flow. Journal of the Optical Society of America A, 7, 160-169.
59. Warren, W. H., Jr., & Kurtz, K. J. (1992). The role of central and peripheral vision in perceiving the direction of self-motion. Perception and Psychophysics, 51, 443-454.
60. Warren, W. H., Jr., Li, L. Y., Ehrlich, S. M., Crowell, J. A., & Banks, M. S. (1996). Perception of heading during eye movements uses both optic flow and eye position information. Investigative Ophthalmology and Vision Science, 37.
61. Waxman, A. M., & Ullman, S. (1985). Surface structure and three-dimensional motion from image flow kinematics. International Journal of Robotics Research, 4, 72-94.
62. Waxman, A. M., & Wohn, K. (1988). Image flow theory: A framework for 3-D inference from time-varying imagery. In C. Brown (Ed.), Advances in computer vision (Vol. 1, pp. 165-224). Hillsdale, NJ: Erlbaum.
63. Zalama, E., Gaudiano, P., & Lopez-Coronado, J. (1995). A real-time, unsupervised neural network for the low-level control of a mobile robot in a nonstationary environment. Neural Networks, 8, 103-123.