Automatic Mineral Identification Using Genetic Programming
Inhaltsverzeichnis
Reference
Brian J. Ross, F. Fueten and D.Y. Yashkir: Automatic Mineral Identification Using Genetic Programming. Journal of Machine Vision and Applications, v.13, n.2, 2001, pp. 61-69.
DOI
http://dx.doi.org/10.1007/PL00013273
http://link.springer.com/article/10.1007%2FPL00013273
Abstract
Automatic mineral identification using evolutionary computation technology is discussed. Thin sections of mineral samples are photographed digitally using a computer-controlled rotating polarizer stage on a petrographic microscope. A suite of image processing functions is applied to the images. Filtered image data for identified mineral grains is then selected for use as training data for a genetic programming system, which automatically synthesizes computer programs that identify these grains. The evolved programs use a decision-tree structure that compares the mineral image values with one other, resulting in a thresholding analysis of the multi-dimensional colour and textural space of the mineral images.
Extended Abstract
Bibtex
Used References
Abramson M, Hunter L (1996) Classification using cultural co-evolution and genetic programming. In: Koza JR, Goldberg D, Fogel D, Riolo R (eds) Genetic Programming 1996: Proceedings of the First Annual Con- ference. MIT Press, Cambridge, Mass., pp 249–254
Autio J, Rantanen L, Visa A, Lukkarinen S (1999) The classification and characterisation of rock using texture analysis by co-occurrence matri- ces and the Hough transform. In: Proceedings Geovision 99: Interna- tional Symposium on Imaging Applications in Geology, University of Liege, Belgium, May 6–7, 1999 pp 5–8
B ̈ack T (1996) Evolutionary algorithms in theory and practice. Oxford Uni- versity Press, New York
B ̈ack T, Hammel U, Schwefel H-P (1997) Evolutionary Computation: com- ments on the History and Current State. IEEE Trans Evol Comput 1:3–17
Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic program- ming – an introduction. Kaufmann, San Francisco
Berry MJA, Linoff G (1997) Data mining techniques. Wiley, New York Fabbri AG (1984) Image processing of geological data. Van Nostrand- Reinhold, Wokingham, U.K.
Freitas AA (1997) A genetic programming framework for two data min- ing tasks: classification and generalized rule induction. In: Koza JR, Deb K, Dorigo M, Fogel D, Garzon M, Iba H, Riolo R (eds) Genetic Programming 1997: Proceedings of the Second Annual Conference. Kaufmann, San Francisco, pp 96–101
Fueten F (1997) A computer controlled rotating polarizer stage for the petrographic microscope. Comput Geosci 23:203–208
Goodchild JS, Fueten F (1998) Edge detection in petrographic images using the rotating polarizer stage. Comput Geosci 24:745–751
Gray HF, Maxwell RJ, Martinez-Perez I, Arus C, Cerdan S (1996) Genetic programming for classification of brain tumours from nuclear mag- netic resonance biopsy spectra. In: Koza JR, Goldberg D, Fogel D, Riolo R (eds) Genetic Programming 1996: Proceedings of the First Annual Conference. MIT Press, Cambridge, Mass., p 424
Holland JH (1992) Adaptation in natural and artificial systems. MIT Press, Cambridge, Mass.
Jain R, Kasturi R, Schunk BG (1995) Machine vision. McGraw-Hill, New York Koza JR (1992) Genetic programming – on the programming of computers by means of natural selection. MIT Press, Cambridge, Mass.
Launeau P, Bouchez J-L, Benn K (1990) Shape preferred orientation of object populations: automatic analysis of digitized images. Tectono- physics 180:201–211
Launeau P, Cruden CA, Bouchez J-L (1994) Mineral recognition in digital images of rocks: a new approach using multichannel classification. Can Mineral 32: 919–933
Luger GF, Stubblefield WA (1998) Artificial intelligence – structures and strategies for complex problem solving. Addison-Wesley, Reading, Mass.
Marschallinger R (1997) Automatic mineral classification in the macro- scopic scale. Comput Geosci 23:119–126
Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs, 3rd edn. Springer, Berlin Heidelberg New York
Mitchell M (1996) An introduction to genetic algorithms. MIT Press, Cam- bridge, Mass.
Montana DJ (1995) Strongly typed genetic programming. Evol Comput 3:199–230
Petruk W (1989) Short course on image analysis applied to mineral and earth sciences. (Short course handbook, vol 16) Mineralogical Associ- ation of Canada, Ottawa
Pfleiderer S, Ball DGA, Bailey RC (1992) AUTO: A computer program for the determination of the two-dimensional auto-correlation function of digital images. Comput Geosci 19:825–829
Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106
Ryu T-W, Eick CF (1996) MASSON: discovering commonalities in collec- tion of objects using genetic programming. In: Koza JR, Goldberg D, Fogel D, Riolo R (eds) Genetic Programming 1996: Proceedings of the First Annual Conference. MIT Press, Cambridge, Mass., pp 200–208
Siegel EV (1994) Competitively evolving decision trees against fixed train- ing cases for natural language processing. In: Kinnear KE Jr (ed) ad- vances in genetic programming. MIT Press, Cambridge, Mass.
Starkey J, Samantary AK (1993) Edge detection in petrographic images. J Microsc 172:263–266
Thompson S, Fueten F, Bockus D (2001) Mineral identification using arti- ficial neural networks and the rotating polarizer stage. Comput Geosci, Elsevier, in press
Whitley D, Goldberg D, Cant ́u-Paz E, Spector L, Parmee I, Beyer H-G (eds) (2000) Proceedings of the genetic and evolutionary computation conference. Kaufmann, San Francisco
Zhao J, Kearney G, Soper A (1996) Emotional expression classification by genetic programming. In: Late Breaking Papers at the Genetic Pro- gramming 1996 Conference. July 28–31, Stanford University, Stanford, CA, pp 197–202
Zongker D, Punch B (1995) LIL-GP 1.0 user’s manual. Department of Computer Science, Michigan State University, East Lansing, MI
Links
Full Text
http://www.cosc.brocku.ca/~bross/research/machvisapps.pdf
