Abstract
An Intelligent Inspection Engine (IIE) for classification of non-regular shaped objects from images is described and evaluated using real-world data from a waste package sorting application. The entire system is self-organizing. Principal component analysis and additional a priori knowledge on color properties are used for feature extraction. As classifiers growing neural networks provide robustness and minimize the number of runs for parameter tuning. We propose a method to encompass feature extraction and classification within a bootstrap procedure. These method reduces the immense memory requirement for the computation of principal components if number and size of training images are huge without to much loss of recognition quality.
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
Leo Breiman. Bias, variance, and arcing classifiers. Technical Report, Department of Statistics, University of California, Berkely, 1996.
Leo Breiman. Bagging predictors, Technical Report, Department of Statistics, University of California, Berkely, 1995.
S. E. Fahlman and C. Lebiere. The Cascade-Correlation Learning Architecture. Tech. Report CMU-CS-90-100, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, Februar 1990.
Jerome H. Friedman. On Bias, Variance, 0/1-loss, and the Curse-of-Dimensionality, Technical Report, Department of Statistics and Stanford Linear Accelerator Center, Stanford University, April 1996.
Stuart Geman, Elie Bienenstock, and René Doursat. Neural networks and the bias/variance dilemma. Neural Computation, 4:1–58, 1992.
J. M. Lange, H.-M. Voigt, and D. Wolf. Growing Artificial Neural Networks Based on Correlation Measures, Task Decomposition and Local Attention Neurons. In Proceedings of the IEEE International Conference on Neural Networks 1994 as Part of the IEEE World Congress on Computational Intelligence, pages 1355–1358, Orlando, 1994. Vol. 2.
J. M. Lange, H.-M. Voigt, and D. Wolf. The tacoma algorithm for reflective growing of neural networks. In Proceedings of the IEE Colloquium on Advances in Neural Networks for Control and Systems, Berlin, IEE-Digest 94/136, pages 2/1–2/2, London, 1994. The Institution of Electrical Engineers.
J. M. Lange, H.-M. Voigt, and D. Wolf. Task Decomposition and Correlations in Growing Artificial Neural Networks. In P. G. M. M. Mariano, editor, Proceedings of the International Conference on Artificial Neural Networks, pages 735–738, Sorrento, Mai 1994. Springer.
J. M. Lange. Cyclical Local Structural Risk Minimization with Growing Neural Networks, Technical Report TR-96-015, International Computer Science Institute, Berkeley, CA, April 1996.
S. K. Nayar, S. A. Nene, and H. Murase. Real-Time 100 Object Recognition System. In Proceedings of ARPA Image Understanding Workshop, San Fransisco, February 1996
S. K. Nayar, H. Murase, and S. A. Nene. Learning, Positioning, and Tracking Visual Appearance. In IEEE International Conference on Robotics and Automation, San Diego, May 1994.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lange, J.M., Voigt, HM., Burkhardt, S., Göbel, R. (1998). Real-Time Real-World Visual Classification — Making Computational Intelligence Fly. In: Polkowski, L., Skowron, A. (eds) Rough Sets and Current Trends in Computing. RSCTC 1998. Lecture Notes in Computer Science(), vol 1424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-69115-4_42
Download citation
DOI: https://doi.org/10.1007/3-540-69115-4_42
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64655-6
Online ISBN: 978-3-540-69115-0
eBook Packages: Springer Book Archive