Case-Based Object Recognition with Application to Biological Images

  • Petra Perner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)


There are many biotechnological applications where 3-dimensional objects are represented as 2-d objects in a digital image. The dynamic and variable nature of the microorganism thus creates a formidable challenge to the design of a robust 2-d image inspection system with the ideal characteristics of high analysis accuracy but wide generalization ability. We have developed a novel case-based object recognition method for this kind of problems. The method is able to recognize objects and learn incrementally cases for the recognition process. Such a procedure requires capturing new cases for the further recognition process in order to be able to handle the variability of the natural objects. We describe the theory behind the method and how it works on our problem of fungi spore recognition. The developed case-based object recognition method is flexible and robust enough to be used for different recognition tasks in biotechnology.


Object Recognition Similarity Measure Image Mining Case- Based Reasoning 


  1. 1.
    Brown: A Survey of Image Registration Techniques. ACM Computer Surveys 24(4), 325–376 (1992)CrossRefGoogle Scholar
  2. 2.
    Olson, C.F., Huttenlocher, D.P.: Automatic Target Recognition by Matching Oriented Edge Pixels. IEEE Transactions on Image Processing 6(1), 103–113 (1997)CrossRefGoogle Scholar
  3. 3.
    Borgefors, G.: Hierarchical Chamfer Matching: A Parametric Edge Detection Algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 10(6), 848–865 (1988)CrossRefGoogle Scholar
  4. 4.
    Perner, P. (ed.): Data Mining on Multimedia Data. LNCS, vol. 2558. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  5. 5.
    Dryden, I.L., Mardia, K.V.: Statistical Shape Analysis. John Wiley & Sons, Chichester (1998)zbMATHGoogle Scholar
  6. 6.
    Wall, K., Daniellson, P.-E.: A fast sequential Method for Polygonal Approximation of digitized Curves. Comput. Graph. Image Process. 28, 220–227 (1984)CrossRefGoogle Scholar
  7. 7.
    Perner, P., Jänichen, S.: Learning of Form Models from Exemplars. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR&SPR 2004. LNCS, vol. 3138, pp. 153–161. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Jänichen, S., Perner, P.: Conceptual Clustering and Case Generalization of 2-dimensional Forms. Journal on Computational Intelligence (to appear, 2006)Google Scholar
  9. 9.
    Fisher, D., Langley, P.: Approaches to conceptual clustering. In: Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, pp. 691–697 (1985)Google Scholar
  10. 10.
    Lba, W., Langley, P.: Unsupervised Learning of Probabilistic Concept Hierarchies. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds.) Machine learning and its applications, Springer, Heidelberg (2001)Google Scholar
  11. 11.
    Perner, P.: Different Learning Strategies in a Case-Based Reasoning System for Image Interpretation. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 251–261. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  12. 12.
    Perner, P., Perner, H., Jänichen, S.: Recognition of Airborne Fungi Spores in Digital Microscopic Images. Artificial Intelligence in Medicine 36(2), 137–157 (2006) (available on-line October 3, 2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Petra Perner
    • 1
  1. 1.Institute of Computer Vision and applied Computer SciencesLeipzig

Personalised recommendations