This Article 
 Bibliographic References 
 Add to: 
Gray-Scale ALIAS
April 1992 (vol. 4 no. 2)
pp. 109-122

Based on the paradigm of collective learning systems, ALIAS (adaptive learning image analysis system) is an adaptive image-processing engine specifically designed to detect anomalies in otherwise normal images and signals. To accomplish this, ALIAS requires only one pass through a training set, which typically consists of less than 100 samples. The original version of ALIAS (1.0) was completed in Apr. 1990 at the Research Institute for Applied Knowledge Processing (FAW) in Ulm, Germany.

[1] P. Bock, "The emergence of artificial intelligence: Learning to learn,"Al Mag.(AAAI), vol. 6, no. 3, pp. 180-190, Fall 1985.
[2] P. Bock, F. Weingard, and J. White, "A universal model for the structure and function of Collective learning systems," inProc. Int. Symp. New Directions in Comput., Trondheim, Norway, Aug. 1985.
[3] P. Bock, "Observation of the properties of a collective learning stochastic automaton," inProc. Int. Inform. Sci. Symp., Patras, Greece, Aug. 1976.
[4] P. Bock, "A perspective on artificial intelligence: Learning to learn,"Ann. Oper. Res., vol. 16, pp. 33-52, Apr. 1988.
[5] P. Bock, "A massively parallel architecture for collective learning systems,"J. Latin Amer. Soc. Artif. Intell., Mar. 1988.
[6] P. Bock, G. Becker, H. Holz, C. J. Kocinski, and R. Rovner, "An application of collective learning systems theory to an adaptive learning image analysis system: Project ALIAS," inProc. Int. Workshop Neuro Nimes '89: Neural Networks and Their Appl., Nimes, France, Nov. 1989.
[7] P. Bock, R. Rovner, C. J. Kocinski, H. Holz, and G. Becker, "A parallel implementation of collective learning systems theory: Adaptive learning image analysis system (ALIAS)," inProc. 1990 ACM Eighteeth Annu. Comput. Sci. Conf., Feb. 1990, Washington DC.
[8] P. Bock, C.J. Kocinski, and R. Rovner, "A performance evaluation of ALIAS for the detection of geometric anomalies on fractal images," inAdvanced Neural Computers. The Netherlands, Elsevier North-Holland, July 1990, pp. 237-246.
[9] P. Bock, H. Holz, R. Rovner, and C. J. Kocinski, "An initial performance evaluation of unsupervised learning with ALIAS," inProc. Int. Joint Conf. Neural Networks, San Diego, CA, June 1990.
[10] D. Chen and A. Bovik, "Visual pattern image coding,"IEEE Trans. Commun., vol. 38, Dec. 1990.
[11] H. Niemann,Klassijikation von Mustern. Berlin, Germany, Springer-Verlag 1983.
[12] P. Bock, R. Klinnert, R. Kober, C. J. Kocinski, R. Rovner, and H. Schmidt, "Sensitivity of ALIAS to small variations in the dimension of fractal images," to be published.
[13] C. J. Kocinski and P. Bock, "The determination of a fast metric for specifying the spatial complexity of binary images," to be published.
[14] M. F. Barnsley, R. L. Devaney, B. B. Mandelbrot, H. O. Peitgen, D. Saupe, and R. F. Voss,The Science of Fractal Inages. Berlin, Germany, Springer-Verlag, 1988.

Index Terms:
collective learning systems; adaptive image-processing engine; training set; binary images; gray-scale version; computerised picture processing; learning systems
P. Bock, R. Klinnert, R. Kober, R.M. Rovner, H. Schmidt, "Gray-Scale ALIAS," IEEE Transactions on Knowledge and Data Engineering, vol. 4, no. 2, pp. 109-122, April 1992, doi:10.1109/69.134248
Usage of this product signifies your acceptance of the Terms of Use.