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Electronic Notes in Theoretical Computer Science (ENTCS)
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CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
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A quick scale-invariant interest point detecting approach
Machine Vision and Applications
SILT: scale-invariant line transform
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On interest point detection under a landmark-based medical image registration context
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A Space Variant Gradient Based Corner Detector for Sparse Omnidirectional Images
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Bivariate feature localization for SIFT assuming a Gaussian feature shape
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Local appearance-based models using high-order statistics of image features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Image matching by multiscale oriented corner correlation
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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PaCT'05 Proceedings of the 8th international conference on Parallel Computing Technologies
Arterial hemodynamic analysis on non-enhanced magnetic resonance angiogram using optical flow
Artificial Life and Robotics
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Many computer vision tasks rely on feature extraction. Inter est points are such features. This paper shows that interest points are geometrically stable under different transformations and have high information content (distinctiveness). These two properties make interest points very successful in the context of image matching. To measure these two properties quantitatively, we introduce two evaluation criteria: repeatability rate and information content.The quality of the interest points depends on the detector used. In this paper several detectors are compared according to the criteria specified above. We determine which detector gives the best results and show that it satisfies the criteria well.