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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