visual tracking
Denis Mareschal
denis at psy.ox.ac.uk
Wed Feb 3 10:47:36 EST 1993
Hi,
A couple of months ago I sent around a request for further information
concerning higher level connectionist approaches to the development of
visual tracking. I received a number of replies spanning the broad range of
fields in which neural network research is being conducted.
I also received a significant number of requests for the resulting
compiled list of references. I am thus posting a list of references resulting
directly and indirectly from my original request. I have also included a few
relevant psychology review papers.
Thanks to all those who replied. Clearly this list is not exhaustive
and if anyone reading it notices an ommission which may be of interest I
would greatly appreciate hearing from them.
Cheers,
Denis Mareschal
Department of Experimental Psychology
South Parks Road
Oxford University
Oxford OX1 3UD
maresch at black.ox.ac.uk
REFERENCES:
Allen, R. B. (1988), Sequential connectionist networks for answering simple
questions about a microworld. In: Proceedings of the Tenth Annual
Conference of the Cognitive Science Society, pp. 489-495, Hillsdale,
NJ: Erlbaum.
Baloch, A. A. & Waxman A. M. (1991). Visual learning, adaptive expectations
and behavioral conditioning of the mobile robot MAVIN, Neural Networks,
vol. 4, pp. 271-302.
Buck, D. S. & Nelson D. E. (1992). Applying the abductory induction mechanism
(AIM) to the extrapolation of chaotic time series. In: Proceedings of
the National Aerospace Electronics Conference (NAECON), 18-22 May,
Dayton, Ohio, vol. 3, pp 910-915.
Bremner, J. G. (1985). Object tracking and search in infancy: A review of data
and a theoretical evaluation, Developmental Review, 5, pp. 371-396
Carpenter, G. A. & Grossberg, S. (1992). Neural Networks for Vision and Image
Processing, Cambridge, MA: MIT Press.
Cleermans, A., Servan-Schreiber, D. & McClelland, J. L. (1989). Finite state
automata and simple recurrent networks, Neural Computation,1, pp 372-
381.
Deno, D. C., Keller, E. L. & Crandall, W. F. (1989). Dynamical neural network
organization of the visual pursuit system, IEEE Transactions on
Biomedical Engineering, vol. 36, pp. 85-91.
Dobnikar, A., Likar, A. & Podbregar, D. (1989). Optimal visual tracking with
artificial neural network. In: First I.E.E. International Conference
on Artificial Neural Networks (conf. Publ. 313), pp 275-279.
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, pp.
179-211.
Ensley, D. & Nelson, D. E. (1992). Applying Cascade-correlation to the
extrapolation of chaotic time series. Proceedings of the Third
Workshop on Neural Networks: Academic/Industrial/NASA/Defense;
10-12 February, Auburn, Alabama.
Fay, D. A. & Waxman, A. M. (1992). Neurodynamics of real-time image velocity
extraction. In: G. A. Carpenter & S. Grossberg (Eds), Neural Networks
for Vision and Image Processing, pp 221-246, Cambridge, MA: MIT Press.
Gordon, Steele, & Rossmiller (1991). Predicting trajectories using recurrent
neural networks. In: Dagli, Kumara, & Shin (Eds), Intelligent Systems
Through Artificial Neural Networks, ASME Press. (Sorry that's the best
I can do for this reference)
Grossberg, S. & Rudd(1989). A neural architecture for visual motion perception:
Neural Networks, 2, pp. 421-450.
Koch, C. & Ullman, S. (1985). Shifts in selective visual attention: towards
the underlying neural circuitry. Human Neurobiology, 4, pp. 219-227.
Lisberger, S. G., Morris, E. J. & Tychsen, L. (1987). Visual motion processing
and sensory-motor integration for smooth pursuit eye movements,
Annual Review of Neuroscience, 10, pp. 97-129.
Lumer, E., D. (1992). The phase tracker of attention. In: Proceedings of the
Fourteenth Annual Conference of the Cognitive Science Society, pp
962-967, Hillsdale, NJ: Erlbaum.
Neilson,P. D., Neilson, M. D. & O'Dwyer, N. J. (1993, in press). What limits
high speed tracking performance?, Human Mouvement Science, 12.
Nelson, D. E., Ensley, D. D. & Rogers, S. K. (1992). Prediction of chaotic time
series using Cascade Correlation: Effects of number of inputs and
training set size. In: The Society for Optical Engineering (SPIE),
Proceeedings of the Applications of Artificial Neural Networks III
Conference, 21-24 April, Orlando, Florida, vol. 1709, pp 823-829.
Marshall, J. A. (1990). Self-organizing neural networks for perception of
visual motion, Neural Networks, 3, pp. 45-74.
Martin, W. N. & Aggarwal, J. K. (Eds) (1988). Motion Understanding: Robot
and Human Vision. Boston: Kluwer Academic Publishers.
Metzgen, Y. & Lehmann D. (1990). Learning temporal sequences by local synaptic
changes, Network, 1, pp. 271-302.
Nakayama, K. (1985). Biological image motion processing: A review. Vision
Research 25, pp 625-660.
Parisi, D., Cecconi, F. & Nolfi, S. (1990). Econets: Neural networks that learn
in an environment, Network, 1, pp. 149-168.
Pearlmutter, B. A. (1989). Learning state space trajectories in recurrent
networks, Neural Computation, 1, pp. 263-269.
Regier, T. (1992). The acquisition of lexical semantics for spatial terms:
A connectionist model of perceptual categorization. International
Computer Science Institute (ICSI) Technical Report TR-92-062, Berkely.
Schmidhuber, J. & Huber, R. (1991). Using adaptive sequential neurocontrol
for efficient learning of translation and rotation invariance. In:
T. Kohonen, K. Makisara, O. Simula & J. Kangas (Eds), Artificial
Neural Networks, pp 315-320, North Holland: Elsevier Science.
Schmidhuber, J. & Huber, R. (1991). Learning to generate artificial foveal
trajectories for target detection. International Journal of Neural
Systems, 2, pp. 135-141.
Schmidhuber, J. & Wahnsiedler, R. (1992). Planning simple trajectories using
neural subgoal generators. Second International Conference on
Simulations of Adaptive Behavior (SAB92). (Available by ftp from Jordan
Pollack's Neuroprose Archive).
Sereno, M. E. (1986). Neural network model for the measurement of visual
motion. Journal of the Optical Sociaty of America A, 3, pp 72.
Sereno, M. E. (1987). Implementing stages of motion analysis in neural.
Program of the Ninth Annual Conference of the Cognitive Science
Society, pp. 405-416, Hillsdala, NJ: Erlbaum.
Servan-Schreiber, D., Cleermans, A. & McClelland, J. L. (1991). Graded state
machines: The representation of temporal contingencies in simple
recurrent networks, 7, pp. 161-193.
Shimohara, K., Uchiyama T. & Tokunaya Y. (1988). Back propagation networks for
event-driven temporal sequence processing. In: IEEE International
Conference on Neural Networks (San Diego), vol. 1, pp. 665-672, NY:
IEEE.
Sutton, R. S. (1988). Learning to predict by the methods of temporal
differences, Machine Learning, 3, pp 9-44.
Tolg, S. (1991). A biological motivated system to track moving objectas by
active camera control. In:T. Kohonen, K. Makisara, O. Simula & J.
Kangas (Eds), Artificial Neural Networks, pp 1237-1240, North Holland:
Elsevier Science.
Wechsler, H. (Ed) (1991). Neural Networks for Human and Machine Perception,
New York: Academic Press.
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