Real-time visual recognition of objects and scenes using P-channel matching

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4522 LNCS, Issue , 2007, Pages 908-917

  Felsberg, Michael ^a   Hedborg, Johan ^a  

^a LINKÖPING UNIVERSITY   (Sweden)

Author keywords

Object recognition; P channels; Real time processing; Scene registration; View based computer vision

Indexed keywords

ALGORITHMS; CAMERAS; DATABASE SYSTEMS; MATHEMATICAL MODELS; REAL TIME SYSTEMS; ROBOTICS;

REAL TIME PROCESSING; SCENE REGISTRATION;

OBJECT RECOGNITION;

EID: 38049010995     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-73040-8_92     Document Type: Conference Paper

References (18)

1. 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. London, UK, BMVA Vol. 1, pp. 1-10 (September 2005)
2. Nene, S.A., Nayar, S.K., Murase, H.: Columbia object image library (coil-100). Technical Report CUCS-006-96 (1996)
3. Pontil, M., Verri, A.: Support vector machines for 3d object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(6), 637-646 (1998)
4. 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)
5. 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)
6. Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, New York (1991)
7. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1995)
8. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
9. Granlund, G.H.: An Associative Perception-Action Structure Using a Localized Space Variant Information Representation. In: Proceedings of Algebraic Frames for the Perception-Action Cycle (AFPAC), Kiel, Germany (September 2000)
10. Snippe, H.P., Koenderink, J.J.: Discrimination thresholds for channel-coded systems. Biological Cybernetics 66, 543-551 (1992)
11. Forssén, P.E.: Low and Medium Level Vision using Channel Representations. PhD thesis, Linköping University, Sweden (2004)
12. 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)
13. 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)
14. Jonsson, E., Felsberg, M.: Reconstruction of probability density functions from channel representations. In: Proc. 14th Scandinavian Conference on Image Analysis (2005)
15. Unser, M.: Splines - a perfect fit for signal and image processing. IEEE Signal Processing Magazine 16, 22-38 (1999)
16. Felsberg, M., Granlund, G.: P-channels: Robust multivariate m-estimation of large datasets. In: International Conference on Pattern Recognition, Hong Kong (August 2006)
17. Granlund, G.H., Knutsson, H.: Signal Processing for Computer Vision. Kluwer Academic Publishers, Dordrecht (1995)
18. Brand, M.: Incremental singular value decomposition of uncertain data with missing values. Technical Report TR-2002-24, Mitsubishi Electric Research Laboratory (2002)