Cartesian Genetic Programming for Image Processing

  • Simon Harding
  • Jürgen Leitner
  • Jürgen Schmidhuber
Chapter
Part of the Genetic and Evolutionary Computation book series (GEVO)

Abstract

Combining domain knowledge about both imaging processing and machine learning techniques can expand the abilities of Genetic Programming when used for image processing. We successfully demonstrate our new approach on several different problem domains. We show that the approach is fast, scalable and robust. In addition, by virtue of using off-the-shelf image processing libraries we can generate human readable programs that incorporate sophisticated domain knowledge.

Key words

Cartesian genetic programming Image processing Object detection 

Notes

Acknowledgements

The authors would like to thank Julian Miller for his help in refining this paper.

References

  1. Bradski G (2000) The OpenCV Library. Dr Dobb’s Journal of Software ToolsGoogle Scholar
  2. Gonzalez RC, Woods RE (2006) Digital Image Processing (3rd Edition). Prentice-Hall, Inc., Upper Saddle River, NJ, USAGoogle Scholar
  3. Harding S (2008) Evolution of image filters on graphics processor units using cartesian genetic programming. In: Wang J (ed) 2008 IEEE World Congress on Computational Intelligence, IEEE Computational Intelligence Society, IEEE Press, Hong Kong, pp 1921–1928, DOI doi:10.1109/CEC. 2008.4631051Google Scholar
  4. Harding S, Banzhaf W, Miller JF (2010a) A survey of self modifying cartesian genetic programming. In: Riolo R, McConaghy T, Vladislavleva E (eds) Genetic Programming Theory and Practice VIII, Genetic and Evolutionary Computation, vol 8, Springer, Ann Arbor, USA, chap 6, pp 91–107, URL http://www.springer.com/computer/ai/book/978-1-4419-7746-5
  5. Harding S, Miller JF, Banzhaf W (2010b) Developments in cartesian genetic programming: self-modifying CGP. Genetic Programming and Evolvable Machines 11(3/4):397–439, DOI doi:10.1007/s10710-010-9114-1, tenth Anniversary Issue: Progress in Genetic Programming and Evolvable MachinesGoogle Scholar
  6. Harding S, Graziano V, Leitner J, Schmidhuber J (2012) Mt-cgp: Mixed type cartesian genetic programming. In: Genetic and Evolutionary Computation Conference: GECCO 2012, Philidelphia, USA, July 2012, ACM PressGoogle Scholar
  7. Leitner J, Harding S, Frank M, Förster A, Schmidhuber J (2012a) Humanoid robot learns visual object localisation. RSS, submittedGoogle Scholar
  8. Leitner J, Harding S, Frank M, Förster A, Schmidhuber J (2012b) icVision: A Modular Vision System for Cognitive Robotics Research. In: International Conference on Cognitive Systems (CogSys)Google Scholar
  9. Leitner J, Harding S, Frank M, Förster A, Schmidhuber J (2012c) Transferring spatial perception between robots operating in a shared workspace. IROS, submittedGoogle Scholar
  10. Martínek T, Sekanina L (2005) An evolvable image filter: Experimental evaluation of a complete hardware implementation in fpga. In: Moreno JM, Madrenas J, Cosp J (eds) ICES, Springer, Lecture Notes in Computer Science, vol 3637, pp 76–85Google Scholar
  11. Matthews BW (1975) Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochimica et Biophysica Acta 405(2):442–451, URL http://www.ncbi.nlm.nih.gov/pubmed/1180967
  12. Miller JF (1999) An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann, Orlando, Florida, USA, vol 2, pp 1135–1142, URL http://citeseer.ist.psu.edu/153431.html
  13. Miller JF (ed) (2011) Cartesian Genetic Programming. Natural Computing Series, Springer, DOI doi:10.1007/978-3-642-17310-3, URL http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7
  14. Miller JF, Smith SL (2006) Redundancy and computational efficiency in cartesian genetic programming. In: IEEE Transactions on Evoluationary Computation, vol 10, pp 167–174CrossRefGoogle Scholar
  15. Poli R (1996) Genetic programming for image analysis. Technical Report CSRP-96-1, University of Birmingham, UK, URL ftp://ftp.cs.bham.ac.uk//pub/tech-reports/1996/CSRP-96-01.ps.gz
  16. Sekanina L, Harding SL, Banzhaf W, Kowaliw T (2011) Image processing and CGP. In: Miller JF (ed) Cartesian Genetic Programming, Natural Computing Series, Springer, chap 6, pp 181–215, DOI doi:10.1007/978-3-642-17310-3-6, URL http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7
  17. Shirakawa S, Nagao T (2007) Feed forward genetic image network: Toward efficient automatic construction of image processing algorithm. In: Bebis G, Boyle R, Parvin B, Koracin D, Paragios N, Tanveer SM, Ju T, Liu Z, Coquillart S, Cruz-Neira C, Muller T, Malzbender T (eds) Advances in Visual Computing: Proceedings of the 3rd International Symposium on Visual Computing (ISVC 2007) Part II, Springer, Lake Tahoe, Nevada, USA, Lecture Notes in Computer Science, vol 4842, pp 287–297, DOI doi:10.1007/978-3-540-76856-2-28, URL http://www.springerlink.com/content/875l8257231732pq/
  18. Shirakawa S, Nakayama S, Nagao T (2009) Genetic image network for image classification. In: Giacobini M, Brabazon A, Cagnoni S, Caro GAD, Ekárt A, Esparcia-Alcázar A, Farooq M, Fink A, Machado P, McCormack J, O’Neill M, Neri F, Preuss M, Rothlauf F, Tarantino E, Yang S (eds) Applications of Evolutionary Computing, EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG, Springer, Tübingen, Germany, Lecture Notes in Computer Science, vol 5484, pp 395–404, DOI doi:10.1007/978-3-642-01129-0-44, URL http://www.springerlink.com/content/r0722q3444788837/
  19. Silva S, Vasconcelos MJ, Melo JB (2010) Bloat free genetic programming versus classification trees for identification of burned areas in satellite imagery. In: Di Chio C, Cagnoni S, Cotta C, Ebner M, Ekart A, Esparcia-Alcazar AI, Goh CK, Merelo JJ, Neri F, Preuss M, Togelius J, Yannakakis GN (eds) EvoIASP, Springer, Istanbul, LNCS, vol 6024, pp 272–281, DOI doi:10.1007/978-3-642-12239-2-28Google Scholar
  20. Slaný K, Sekanina L (2007) Fitness landscape analysis and image filter evolution using functional-level CGP. In: Ebner M, O’Neill M, Ekárt A, Vanneschi L, Esparcia-Alcázar AI (eds) Proceedings of the 10th European Conference on Genetic Programming, Springer, Valencia, Spain, Lecture Notes in Computer Science, vol 4445, pp 311–320, DOI doi: 10.1007/978-3-540-71605-1-29Google Scholar
  21. Smith SL, Leggett S, Tyrrell AM (2005) An implicit context representation for evolving image processing filters. In: Rothlauf F, Branke J, Cagnoni S, Corne DW, Drechsler R, Jin Y, Machado P, Marchiori E, Romero J, Smith GD, Squillero G (eds) Applications of Evolutionary Computing, EvoWorkshops2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC, Springer Verlag, Lausanne, Switzerland, LNCS, vol 3449, pp 407–416, DOI doi:10.1007/b106856Google Scholar
  22. Spina TV, Montoya-Zegarra JA, Falcao AX, Miranda PAV (2009) Fast interactive segmentation of natural images using the image foresting transform. In: 16th International Conference on Digital Signal Processing, pp 1–8, DOI doi:10.1109/ICDSP.2009.5201044Google Scholar
  23. Uto K, Kosugi Y, Ogatay T (2009) Evaluation of oak wilt index based on genetic programming. In: First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS ’09, pp 1–4, DOI doi:10.1109/WHISPERS.2009.5289107Google Scholar
  24. Vasicek Z, Sekanina L (2007) Evaluation of a new platform for image filter evolution. In: Adaptive Hardware and Systems, 2007. AHS 2007. Second NASA/ESA Conference on, pp 577–586, DOI 10.1109/AHS.2007.49Google Scholar
  25. Wang2.
    Wang2 J, Tan Y (2011) Morphological image enhancement procedure design by using genetic programming. In: Krasnogor N, Lanzi PL, Engelbrecht A, Pelta D, Gershenson C, Squillero G, Freitas A, Ritchie M, Preuss M, Gagne C, Ong YS, Raidl G, Gallager M, Lozano J, Coello-Coello C, Silva DL, Hansen N, Meyer-Nieberg S, Smith J, Eiben G, Bernado-Mansilla E, Browne W, Spector L, Yu T, Clune J, Hornby G, Wong ML, Collet P, Gustafson S, Watson JP, Sipper M, Poulding S, Ochoa G, Schoenauer M, Witt C, Auger A (eds) GECCO ’11: Proceedings of the 13th annual conference on Genetic and evolutionary computation, ACM, Dublin, Ireland, pp 1435–1442, DOI doi:10.1145/2001576.2001769Google Scholar
  26. Wijesinghe G, Ciesielski V (2007) Using restricted loops in genetic programming for image classification. In: Srinivasan D, Wang L (eds) 2007 IEEE Congress on Evolutionary Computation, IEEE Computational Intelligence Society, IEEE Press, Singapore, pp 4569–4576, DOI doi:10. 1109/CEC.2007.4425070Google Scholar
  27. Wikipedia (2012) Matthews correlation coefficient — wikipedia, the free encyclopedia. URL http://www.en.wikipedia.org/w/index.php?title=Matthews-correlat ion-coefficientoldid=481532406, [Online; accessed 21-March-2012]
  28. Zhang M, Ciesielski VB, Andreae P (2003) A domain-independent window approach to multiclass object detection using genetic programming. EURASIP Journal on Applied Signal Processing 2003(8):841–859, DOI doi:10.1155/S1110865703303063, URL http://www.mcs.vuw.ac.nz/~pondy/eurasip2003.pdf, special Issue on Genetic and Evolutionary Computation for Signal Processing and Image Analysis

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Simon Harding
    • 1
  • Jürgen Leitner
    • 1
  • Jürgen Schmidhuber
    • 1
  1. 1.Dalle Molle Institute for Artificial Intelligence (IDSIA)MannoSwitzerland

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