Abstract
In this paper we propose a new approach to real-time view-based object recognition and scene registration. Object recognition is an important sub-task in many applications, as e.g., robotics, retrieval, and surveillance. Scene registration is particularly useful for identifying camera views in databases or video sequences. All of these applications require a fast recognition process and the possibility to extend the database with new material, i.e., to update the recognition system online.
The method that we propose is based on P-channels, a special kind of information representation which combines advantages of histograms and local linear models. Our approach is motivated by its similarity to information representation in biological systems but its main advantage is its robustness against common distortions as clutter and occlusion. The recognition algorithm extracts a number of basic, intensity invariant image features, encodes them into P-channels, and compares the query P-channels to a set of prototype P-channels in a database. The algorithm is applied in a cross-validation experiment on the COIL database, resulting in nearly ideal ROC curves. Furthermore, results from scene registration with a fish-eye camera are presented.
This work has been supported by EC Grants IST-2003-004176 COSPAL and IST-2002-002013 MATRIS. This paper does not represent the opinion of the European Community, and the European Community is not responsible for any use which may be made of its contents.
Chapter PDF
Similar content being viewed by others
Keywords
References
Obdržálek, Š., Matas, J.: Sub-linear indexing for large scale object recognition. In: Clocksin, W.F., Fitzgibbon, A.W., Torr, P.H.S. (eds.) BMVC 2005: Proceedings of the 16th British Machine Vision Conference, vol. 1, September 2005, pp. 1–10. BMVA, London (2005)
Nene, S.A., Nayar, S.K., Murase, H.: Columbia object image library (coil-100). Technical Report CUCS-006-96 (1996)
Pontil, M., Verri, A.: Support vector machines for 3d object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(6), 637–646 (1998)
Roobaert, D., Zillich, M., Eklundh, J.O.: A pure learning approach to background-invariant object recognition using pedagogical support vector learning. In: IEEE Computer Vision and Pattern Recognition, vol. 2, pp. 351–357 (2001)
Murphy-Chutorian, E., Aboutalib, S., Triesch, J.: Analysis of a biologically-inspired system for real-time object recognition. Cognitive Science Online 3, 1–14 (2005)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, New York (1991)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1995)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Granlund, G.H.: An Associative Perception-Action Structure Using a Localized Space Variant Information Representation. In: Sommer, G., Zeevi, Y.Y. (eds.) AFPAC 2000. LNCS, vol. 1888, pp. 48–68. Springer, Heidelberg (2000)
Snippe, H.P., Koenderink, J.J.: Discrimination thresholds for channel-coded systems. Biological Cybernetics 66, 543–551 (1992)
Forssén, P.E.: Low and Medium Level Vision using Channel Representations. PhD thesis, Linköping University, Sweden (2004)
Johansson, B., Elfving, T., Kozlov, V., Censor, Y., Forssén, P.E., Granlund, G.: The application of an oblique-projected landweber method to a model of supervised learning. Mathematical and Computer Modelling 43, 892–909 (2006)
Felsberg, M., Forssén, P.E., Scharr, H.: Channel smoothing: Efficient robust smoothing of low-level signal features. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(2), 209–222 (2006)
Jonsson, E., Felsberg, M.: Reconstruction of Probability Density Functions from Channel Representations. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 491–500. Springer, Heidelberg (2005)
Unser, M.: Splines – a perfect fit for signal and image processing. IEEE Signal Processing Magazine 16, 22–38 (1999)
Felsberg, M., Granlund, G.: P-channels: Robust multivariate m-estimation of large datasets. In: International Conference on Pattern Recognition, Hong Kong (August 2006)
Granlund, G.H., Knutsson, H.: Signal Processing for Computer Vision. Kluwer Academic Publishers, Dordrecht (1995)
Brand, M.: Incremental singular value decomposition of uncertain data with missing values. Technical Report TR-2002-24, Mitsubishi Electric Research Laboratory (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Felsberg, M., Hedborg, J. (2007). Real-Time Visual Recognition of Objects and Scenes Using P-Channel Matching. In: Ersbøll, B.K., Pedersen, K.S. (eds) Image Analysis. SCIA 2007. Lecture Notes in Computer Science, vol 4522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73040-8_92
Download citation
DOI: https://doi.org/10.1007/978-3-540-73040-8_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73039-2
Online ISBN: 978-3-540-73040-8
eBook Packages: Computer ScienceComputer Science (R0)
