Democratic integration: Self-organized integration of adaptive cues

Neural Computation

Volumn 13, Issue 9, 2001, Pages 2049-2074

  Triesch, Jochen ^a   Von Der Malsburg, Christoph ^b,c  

^a University of Rochester   (United States)

^b RUHR UNIVERSITY BOCHUM   (Germany)

^c UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ASSOCIATION; BIOLOGICAL MODEL; CONTRAST SENSITIVITY; FACE; HUMAN; MOVEMENT PERCEPTION; PATTERN RECOGNITION; PERCEPTION; PHYSIOLOGY; VISION;

CONTRAST SENSITIVITY; CUES; FACE; HUMANS; MODELS, NEUROLOGICAL; MOTION PERCEPTION; PATTERN RECOGNITION, VISUAL; PERCEPTION; VISUAL PERCEPTION;

EID: 0035463825     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976601750399308     Document Type: Article

References (28)

1. Atkins, J. E., Fiser, J., & Jacobs, R. A. (2001). Experience-dependent visual cue integration based on consistencies between visual and haptic percepts. Vision Research, 41, 459-471.
2. Boehme, H.-J., Braumann, U.-D., Brakensiek, A., Corradini, A., Krabbes, M., & Gross, H.-M. (1998). User localisation for visually-based human-machine-interaction. In Proceedings of FG'98, The IEEE Third International Conference on Automatic Face and Gesture Recognition (pp. 486-491).
3. Bower, T. G. R. (1974). The evolution of sensory systems. In R. B. MacLeod, & H. L. J. Pick (Eds.), Perception: Essays in honor of James J. Gibson (pp. 141-152). Ithaca, NY: Cornell University Press.
4. Dasarathy, B. V. (1997). Sensor fusion potential exploitation - innovative architectures and illustrative applications. Proceedings of the IEEE, 85(1), 24-38.
5. de Sa, V. R. (1994). Unsupervised classification learning from cross-modal environmental structure. Unpublished doctoral dissertation, University of Rochester, New York.
6. de Sa, V. R., & Ballard, D. H. (1998). Category learning through multi-modality sensing. Neural Computation, 10(5).
7. Ernst, M. O., Banks, M. S., & Bülthoff, H. H. (2000). Touch can change visual slant perception. Nature Neuroscience, 3(1), 69-73.
8. Feyrer, S., & Zell, A. (1998). Ein integrierter Ansatz zur Lokalisierung von Personen in Bildfolgen. In S. Posch & H. Ritter (Eds.), Dynamische Perzeption (pp. 183-190). Sankt Augustin, Germany: infix.
9. Fine, I., & Jacobs, R. A. (1999). Modeling the combination of motion, stereo, and vergence angle cues to visual depth. Neural Computation, 11, 1297-1330.
10. Gonzales, R. C., & Woods, R. E. (1992). Digital image processing. Reading, MA: Addison-Wesley.
11. Hall, D. L., & Llinas, J. (1997). An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), 6-23.
12. Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991). Adaptive mixtures of local experts. Neural Computation, 3, 79-87.
13. Jetto, L., Longhi, S., & Venturini, G. (1999). Development and experimental validation of an adaptive extended Kalman filter for the localization of mobile robots. IEEE Transactions on Robotics and Automation, 15, 219-229.
14. Kam, M., Zhu, X., & Kalata, P. (1997). Sensor fusion for mobile robot navigation. Proc. of the IEEE, 85(1), 108-118.
15. Luo, R. C., & Kay, M. G. (1989). Multisensor integration and fusion in intelligent systems. IEEE Transactions on Systems, Man, and Cybernetics, 19(5), 901-931.
16. McKenna, S. J., Gong, S., & Raja, Y. (1998). Modelling facial colour and identity with gaussian mixtures. Pattern Recognition, 32(12), 1883-1892.
17. Murphy, R. R. (1996). Biological and cognitive foundations of intelligent sensor fusion. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 26(1), 42-51.
18. Schnittger, T., & Handmann, U. (1998). Fusion von Bildanalyseverfahren mittels einer Neuronalen Kopplungsstruktur (Internal Rep. 98-01). Bochum, Germany: Institut für Neuroinformatik, Ruhr-Universtität Bochum. (in German)
19. Steffens, J., Elagin, E., & Neven, H. (1998). Personspotter - fast and robust system for human detection, tracking and recognition. In Proceedings of FG'98, The IEEE Third International Conference on Automatic Face and Gesture Recognition (pp. 516-521).
20. Steinhage, A., Winkel, C., & Gorontzi, K. (1999). Attractor dynamics to fuse strongly perturbed sensor data. In Proceedings of the IEEE 1999 International Geoscience and Remote Sensing Symposium, Hamburg (pp. 1217-1219).
21. Sundareshan, M. K., & Amoozegar, F. (1997). Neural network fusion capabilities for efficient implementation of tracking algorithms. Optical Engineering, 36(3), 692-707.
22. Tiean, B., Shaikh, M., Azimi-Sadjadi, M., Haar, T. V., & Reinke, D. (1999). A study of cloud classification with neural networks using spectral and textural features. IEEE Transactions on Neural Networks, 10(1), 138-151.
23. Triesch, J. (1999). Vision-based robotic gesture recognition. Germany: Shaker-Verlag Aachen.
24. Triesch, J. (2000). Democratic integration: A theory of adaptive sensory integration (Tech. Rep. No. NRL TR 00.1). Rochester, NY: National Resource Laboratory for the Study of Brain and Behavior, University of Rochester.
25. Triesch, J., & von der Malsburg, C. (2000). Self-organized integration of adaptive visual cues for face tracking. In Proceedings of the Fourth International Conference on Automatic Face and Gesture Recognition, Grenoble, France, March 28-30 2000 (pp. 102-107). IEEE Computer Society.
26. von der Malsburg, C. (1981). The correlation theory of brain function (Internal Rep. 81-2), Göttingen, Germany: Department of Neurobiology, Max-Planck-Institute for Biophysical Chemistry.
27. Vorbrüggen, J. C. (1995). Zwei Modelle zur datengetriebenen Segmentierung visueller Daten. Frankfurt am Main, Germany: Verlag Harri Deutsch, Thun.
28. Williams, A., Pachowicz, P., & Ronk, L. (1998). Expert assisted, adaptive, and robust fusion. Optical Engineering, 37(2), 378-390.