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Support Vector Machines for 3D Object Recognition
June 1998 (vol. 20 no. 6)
pp. 637-646
ASCII Text
x 
 
Massimiliano Pontil, Alessandro Verri, "Support Vector Machines for 3D Object Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 6, pp. 637-646, June, 1998.
 
 
BibTex
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@article{ 10.1109/34.683777,
author = {Massimiliano Pontil and Alessandro Verri},
title = {Support Vector Machines for 3D Object Recognition},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {20},
number = {6},
issn = {0162-8828},
year = {1998},
pages = {637-646},
doi = {http://doi.ieeecomputersociety.org/10.1109/34.683777},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Support Vector Machines for 3D Object Recognition
IS - 6
SN - 0162-8828
SP637
EP646
EPD - 637-646
A1 - Massimiliano Pontil,
A1 - Alessandro Verri,
PY - 1998
KW - Support vector machines
KW - optimal separating hyperplane
KW - appearance-based object recognition
KW - pattern recognition.
VL - 20
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 

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 7,200 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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Index Terms:
Support vector machines, optimal separating hyperplane, appearance-based object recognition, pattern recognition.
Citation:
Massimiliano Pontil, Alessandro Verri, "Support Vector Machines for 3D Object Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 6, pp. 637-646, June 1998, doi:10.1109/34.683777
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