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Towards the Visualisation of Shape Features The Scope Histogram

  • Arne Schuldt
  • Björn Gottfried
  • Otthein Herzog
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4314)

Abstract

Classifying objects in computer vision, we are faced with a great many features one can use. This paper argues that diagrammatic representations help to comprehend properties of features. This is important for the purpose of deciding which features should be used for a given classification task. We introduce such a diagrammatic representation for a shape feature and show how it enables one to decide whether this feature helps to distinguish some categories given. Additionally, it shows that the proposed feature keeps up with other features falling into the same complexity class.

Keywords

Diagrammatic Representation Correct Match Reference Segment Atomic Relation Closed Polygon 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Allen, J.F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM 26(11), 832–843 (1983)zbMATHCrossRefGoogle Scholar
  2. 2.
    Attneave, F.: Some Informational Aspects of Visual Perception. Psychological Review 61, 183–193 (1954)CrossRefGoogle Scholar
  3. 3.
    Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, Chichester (1973)zbMATHGoogle Scholar
  4. 4.
    Freksa, C.: Temporal Reasoning Based on Semi-Intervals. Artificial Intelligence 54(1), 199–227 (1992)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Garson, G.D., Biggs, R.S.: Analytic Mapping and Geographic Databases. Sage Publications, Newbury Park (1992)Google Scholar
  6. 6.
    Gottfried, B.: Reasoning about Intervals in Two Dimensions. In: Thissen, W., et al. (eds.) IEEE International Conference on Systems, Man and Cybernetics, The Hague, pp. 5324–5332. IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
  7. 7.
    Gottfried, B.: Shape from Positional-Contrast — Characterising Sketches with Qualitative Line Arrangements. Deutscher Universitäts-Verlag, Wiesbaden (2007)Google Scholar
  8. 8.
    Larkin, J.H., Simon, H.A.: Why a Diagram is (Sometimes) Worth Ten Thousand Words. Cognitive Science 11, 65–99 (1987)CrossRefGoogle Scholar
  9. 9.
    Latecki, L.J., Lakämper, R.: Shape Similarity Measure Based on Correspondence of Visual Parts. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1185–1190 (2000)CrossRefGoogle Scholar
  10. 10.
    Latecki, L.J., Lakämper, R., Eckhardt, U.: Shape Descriptors for Non-rigid Shapes with a Single Closed Contour. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 424–429. IEEE Computer Society Press, Los Alamitos (2000)Google Scholar
  11. 11.
    Mitzias, D.A., Mertzios, B.G.: Shape Recognition with a Neural Classifier Based on a Fast Polygon Approximation Technique. Pattern Recognition 27, 627–636 (1994)CrossRefGoogle Scholar
  12. 12.
    Zimmermann, K., Freksa, C.: Qualitative Spatial Reasoning Using Orientation, Distance, and Path Knowledge. Applied Intelligence 6, 49–58 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Arne Schuldt
    • 1
  • Björn Gottfried
    • 1
  • Otthein Herzog
    • 1
  1. 1.Centre for Computing Technologies (TZI), University of Bremen, Am Fallturm 1, D-28359 Bremen 

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