The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Support vector machines for 3D object recognition
Abstract
Support vector machines (SVMs) have been recently proposed as a new technique for pattern recognition. Intuitively, given a set of points which belong to either of two classes, a linear SVM finds the hyperplane leaving the largest possible fraction of points of the same class on the same side, while maximizing the distance of either class from the hyperplane. The hyperplane is determined by a subset of the points of the two classes, named support vectors, and has a number of interesting theoretical properties. In this paper, we use linear SVMs for 3D object recognition. We illustrate the potential of SVMs on a database of 7200 images of 100 different objects. The proposed system does not require feature extraction and performs recognition on images regarded as points of a space of high dimension without estimating pose. The excellent recognition rates achieved in all the performed experiments indicate that SVMs are well-suited for aspect-based recognition.
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Cites
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Cited by
Zhang, J., Marszałek, M., Lazebnik, S., & Schmid, C. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-9794-4 · Cited by 1,923
Learning methods for generic object recognition with invariance to pose and lighting
LeCun, Y., Huang, F. J., & Bottou, L. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315150 · Cited by 1,324
Kotsia, I., & Pitas, I. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.884954 · Cited by 687
Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy
Bruzzone, L., & Marconcini, M. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.57 · Cited by 591
50 Years of object recognition: Directions forward
Andreopoulos, A., & Tsotsos, J. K. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.04.005 · Cited by 347
Metadata
- Authors
- Pontil, M., & Verri, A.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1998-06
- Volume
- 20
- Issue
- 6
- Pages
- 637-646
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2120515362
- Cited by
- 812
- References
- 4
- FWCI
- 19.62
- Type
- journal article
- Language
- en
- Open access
- closed
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