Abstract
In an ongoing research we address the problem of representation and processing of motion information from an integrated perspective covering the range from early visual processing to higher-level cognitive aspects. Here we present experiments that were conducted to investigate the representation and processing of spatio-temporal information. Whereas research in this field is typically concerned with the formulation and implementation of visual algorithms like, e.g., navigation by an analysis of the retinal flow pattern caused by locomotion, we are interested in memory based capabilities, like the recognition of complicated gestures [16].
The result of this array of experiments will deliver a subset of parameters used for the training of an artificial neural network model. Alternatively, these parameters are important for determining the ranges of symbolic descriptions like, e.g., the qualitative approach by [11] in order to provide an user interface matched to conditions in human vision. The architecture of the neural net will be briefly sketched. Its output will be used as input for a higher-level stage modelled with qualitative means.
Not only for itclassification, also for prediction (which is a classi.cation of incomplete data) spatio-temporal information has to be stored for subsequent processes operating on it.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
E. L. Bjork and R. A. Bjork, editors. Memory, Handbook of Perception and Cognition. Academic Press, 2nd edition, 1996.
G. Briscoe. Adaptive Behavioural Cognition. PhD thesis, Curtin University of Technology, School of Computing, September 1997.
A. Eisenkolb, A. Musto, K. Schill, D. Hernandez, and W. Brauer. Representational Levels for the Perception of the Courses of Motion. In C. Freksa, C. Habel, and K. F. Wender, editors, Spatial Cognition, volume I of Lecture Notes in Artificial Intelligence, pages 129–155. Springer Verlag, 1998.
A. Eisenkolb, C. Zetzsche, A. Musto, W. Brauer, and K. Schill. Analysis and modeling of the visual processing of spatiotemporal information: Influence of temporal and spatial separation on the discrimination of trajectories. In Perception, volume 27, page 168, 1998.
M. Ferraro and D. H. Foster. Discrete and continuous modes of curved-line discrimination controlled by effective stimulus duration. Spatial Vision, 1(3):219–230, 1986.
J. J. Freyd. Dynamic mental representations. Psychological Review, 94(4):427–438, 1987.
B. Fritzke. Growing self-organizing networks-why? InM. Verleysen, editor, ESANN’96, pages 61–72. D-Facto Publishers, 1996. http://www.neuroinformatik.ruhr-uni-bochum.de.
S. Grossberg. Competitive learning: From interactive activation to adaptive resonance. In S. Grossberg, editor, Neural Networks and Natural Intelligence, pages 213–250. MIT-Press, 1988.
R. N. Haber. The impending demise of the icon: A critique of the concept of iconic storage in visual information processing. The Behavioral and Brain Sciences, 6:1–54, 1983.
G. Johansson. Visual perception of biological motion and a model for its analysis. Perception & Psychophysics, 14: 201–211, 1973.
A. Musto, K. Stein, A. Eisenkolb, K. Schill, and W. Brauer. Generalization, segmentation and classification of qualitative motion data. In Henri Prade, editor, Proc. of the 13th European Conference on Artificial Intelligence (ECAI-98). John Wiley & Sons, 1998.
K. Nakayama. Biological Image Motion Processing: A Review. Vision Research, 25(5): 625–660, 1985.
C. M. Privitera and L. Shastri. Temporal compositional processing by a dsom hierarchical model. Technical Report 94704-1198, Int. Computer Science Inst. Berkeley, California, Center St.-Suite 600-Berkeley, 1996.
H. Raith. Methoden zur Analyse von Bewegungsinformationen in technischen Systemen und Implementierung eines biologischen Modells zur Repräsentation spatio-temporaler Informationen. Master’s thesis, Institut für Informatik, Technische Universität München, 1999.
D. Regan. Storage of spatial-frequency information and spatial-frequency discrimination. Journal of the Optical Society of America, 2(4): 619–621, 1985.
K. Schill and C. Zetzsche. A model of visual spatio-temporal memory: The icon revisited. Psychological Research, 57:88–102, 1995.
K. Schill, C. Zetzsche, W. Brauer, A. Eisenkolb, and A. Musto. Visual representation of spatio-temporal structure. In B.E. Rogowitz and T.N. Papas, editors, Human Vision and Electronic Imaging, pages 128–138. Proceedings of SPIE, 1998.
G. Sperling. The information available in a brief visual presentation. Psychol. Monogr.: Gen. Applied, 74: 1–29, 1960.
J. Tani. Model-based learning for mobile robot navigation from the dynamical systems perspective. IEEE Trans. System, Man and Cybernetics (Part B), 26(3) 421–436, 1996.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Eisenkolb, A., Schill, K., Röhrbein, F., Baier, V., Musto, A., Brauer, W. (2000). Visual Processing and Representation of Spatio-temporal Patterns. In: Freksa, C., Habel, C., Brauer, W., Wender, K.F. (eds) Spatial Cognition II. Lecture Notes in Computer Science(), vol 1849. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45460-8_11
Download citation
DOI: https://doi.org/10.1007/3-540-45460-8_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67584-6
Online ISBN: 978-3-540-45460-1
eBook Packages: Springer Book Archive