Abstract
Model-based image recognition requires a general model of the object that should be detected in an image. In many applications such models are not known a-priori instead of they must be learnt from examples. Real world applications such as the recognition of biological objects in images cannot be solved by one general model but a lot of different models are necessary in order to handle the natural variations of the appearance of the objects of a certain class. Therefore we are talking about case-based object recognition. In this paper we describe how the shape of an object can be extracted from images and input into a case description. These acquired cases we mine for more general shapes so that at the end a case base of shapes can be constructed and applied for case-based object recognition.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Veltkamp, R.C.: Shape Matching: Similarity Measures and Algorithms. Shape Modelling International, 188–197 (2001)
Rangarajan, A., Chui, H., Bookstein, F.L.: The Softassign Procrustes Matching Algorithm. In: Proc. Information Processing in Medical Imaging, pp. 29–42 (1997)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. In: 1st International Conference on Computer Vision, London, pp. 259–268 (1987)
Cheng, D.-C., Schmidt-Trucksäss, A., Cheng, K.-S., Burkhardt, H.: Using Snakes to Detect the Intimal and Aventitial Layers of the Common Carotid Artery Wall in Sonographic Images. Computer Methods and Programs in Biomedicine 67, 27–37 (2002)
Kendall, D.G.: A Survey of the Statistical Theory of Shape. Statistical Science 4(2), 87–120 (1989)
Bookstein, F.L.: Size and Shape Spaces for Landmark Data in Two Dimensions. Statistical Science 1(2), 181–242 (1986)
Dryden, I.L., Mardia, K.V.: Statistical Shape Analysis. John Wiley & Sons Inc., Chichester (1998)
Cootes, T.F., Taylor, C.J.: A Mixture Model for Representing Shape Variation. Image and Vision Computing 17(8), 567–574 (1999)
Feldmar, J., Ayache, N.: Rigid, Affine and Locally Affine Registration of Free-Form Surfaces. The International Journal of Computer Vision 18(3), 99–119 (1996)
Hill, A., Taylor, C.J., Brett, A.D.: A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(3), 241–251 (2000)
Mortensen, E.N., Barrett, W.A.: Intelligent Scissors for Image Composition. In: Computer Graphics Proceedings, pp. 191–198 (1995)
Haenselmann, T., Effelsberg, W.: Wavelet-Based Semi-Automatic Live-Wire Segmentation. In: Proceedings of the SPIE Human Vision and Electronic Imaging VII, vol. 4662, pp. 260–269 (2003)
Bookstein, F.L.: Landmark Methods for Forms without Landmarks: Morphometrics of Group Differences in Outline Shape. Medical Image Analysis 1(3), 225–244 (1997)
Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition Using Shape Contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(24), 509–522 (2002)
Rangarajan, A., Chui, H., Bookstein, F.L.: The Softassign Procrustes Matching Algorithm. In: Proc. Information Processing in Medical Imaging, pp. 29–42 (1997)
Huttenlocher, D., Klanderman, G., Rucklidge, W.: Comparing Images Using the Hausdorff Distance. IEEE Trans. Pattern Analysis and Machine Intelligence 15(9), 850–863 (1993)
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)
Mokhtarian, F., Abbasi, S., Kittler, J.: Efficient and Robust Retrieval by Shape Content through Curvature Scale Space. In: Proc. International Workshop on Image Databases and Multimedia Search, pp. 35–42 (1996)
Besl, P., McKay, N.: A Method for Registration of 3-D Shapes. IEEE Trans. Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)
Petrou, M., Bosdogianni, P.: Image Processing – The Fundamentals. John Wiley & Sons Inc, Chichester (1999)
Wall, K., Daniellson, P.-E.: A Fast Sequential Method For Polygonal Approximation of Digitized Curves. Comput. Graph. Image Process. 28, 220–227 (1984)
Lele, S.R., Richtsmeier, J.T.: An Invariant Approach to Statistical Analysis of Shapes. Chapman & Hall / CRC (2001)
Perner, P.: Data Mining on Multimedia Data. Springer, Berlin (1998)
Alt, H., Guibas, L.J.: Discrete Geometric Shapes: Matching, Interpolation and Approximation. In: Sack, J.-R., Urrutia, J. (eds.) Handbook of Computational Geometry, pp. 121–153. Elsevier Science Publishers B.V, Amsterdam (1996)
Sclaroff, S., Pentland, A.: Modal Matching for Correspondence and Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 17(6), 545–561 (1995)
Perner, P., Bühring, A.: Case-Based Object Recognition. In: ECCBR 2004 (accepted)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall Inc., Englewood Cliffs (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jänichen, S., Perner, P. (2004). Case Acquisition and Case Mining for Case-Based Object Recognition. In: Funk, P., González Calero, P.A. (eds) Advances in Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science(), vol 3155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_45
Download citation
DOI: https://doi.org/10.1007/978-3-540-28631-8_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22882-0
Online ISBN: 978-3-540-28631-8
eBook Packages: Springer Book Archive