DocumentCode :
561175
Title :
A Robust Affine Invariant Feature Matching Approach
Author :
Gao, Ce ; Song, Yixu ; Jia, Peifa
Author_Institution :
Tsinghua Nat. Lab. for Inf. Sci. & Technol., Tsinghua Univ., Beijing, China
Volume :
1
fYear :
2011
fDate :
18-21 Dec. 2011
Firstpage :
157
Lastpage :
160
Abstract :
Affine transformation detection can be used in many computer vision and other applications. This paper presents a new method for affine transformation detection. The state-of-the-art methods are mainly divided into two classes. One class is based on complicated descriptors. But this kind of methods need a lot of time to establish and matching the complicated descriptors. The second class is based on probabilistic model. But these methods can not yield good matching result in some difficult conditions. Our method tries to combine the two kinds of methods together, so as to acquire the accuracy and efficiency at the same time.
Keywords :
computer vision; image matching; object detection; probability; transforms; affine transformation detection; computer vision; probabilistic model; robust affine invariant feature matching; Accuracy; Computer vision; Detectors; Histograms; Probabilistic logic; Robustness; Training; Affine Invariance; Feature Matching; Learning-Based;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications and Workshops (ICMLA), 2011 10th International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
978-1-4577-2134-2
Type :
conf
DOI :
10.1109/ICMLA.2011.21
Filename :
6146961
Link To Document :
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