DocumentCode :
2400823
Title :
A fast local descriptor for dense matching
Author :
Tola, Engin ; Lepetit, Vincent ; Fua, Pascal
Author_Institution :
Comput. Vision Lab., Ecole Polytech. Fed. de Lausanne, Lausanne
fYear :
2008
fDate :
23-28 June 2008
Firstpage :
1
Lastpage :
8
Abstract :
We introduce a novel local image descriptor designed for dense wide-baseline matching purposes. We feed our descriptors to a graph-cuts based dense depth map estimation algorithm and this yields better wide-baseline performance than the commonly used correlation windows for which the size is hard to tune. As a result, unlike competing techniques that require many high-resolution images to produce good reconstructions, our descriptor can compute them from pairs of low-quality images such as the ones captured by video streams. Our descriptor is inspired from earlier ones such as SIFT and GLOH but can be computed much faster for our purposes. Unlike SURF which can also be computed efficiently at every pixel, it does not introduce artifacts that degrade the matching performance. Our approach was tested with ground truth laser scanned depth maps as well as on a wide variety of image pairs of different resolutions and we show that good reconstructions are achieved even with only two low quality images.
Keywords :
correlation methods; estimation theory; graph theory; image matching; image reconstruction; image resolution; correlation window; dense wide-baseline matching; fast local image descriptor; graph-cuts-based dense depth map estimation algorithm; image quality; image reconstruction; image resolution; Computational efficiency; Computer vision; Feeds; Histograms; Image reconstruction; Laboratories; Layout; Pixel; Robustness; Yield estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
Conference_Location :
Anchorage, AK
ISSN :
1063-6919
Print_ISBN :
978-1-4244-2242-5
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2008.4587673
Filename :
4587673
Link To Document :
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