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Geometric Information Criterion for Model Selection
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Scale-Space Theory in Computer Vision
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IEEE Transactions on Image Processing
Comparing salient point detectors
Pattern Recognition Letters
IMPSAC: Synthesis of Importance Sampling and Random Sample Consensus
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Scale & Affine Invariant Interest Point Detectors
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Moment invariants for recognition under changing viewpoint and illumination
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Recognition invariance obtained by extended and invariant features
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A Performance Evaluation of Local Descriptors
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One-Shot Learning of Object Categories
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International Journal of Computer Vision
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Computer Vision and Image Understanding
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Foundations and Trends® in Computer Graphics and Vision
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ACM Computing Surveys (CSUR)
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Information Sciences: an International Journal
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Visual language modeling for image classification
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Pattern Recognition
Foundations and Trends® in Computer Graphics and Vision
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Pattern Recognition Letters
Local velocity-adapted motion events for spatio-temporal recognition
Computer Vision and Image Understanding
Relative scale method to locate an object in cluttered environment
Image and Vision Computing
Fast pattern recognition using normalized grey-scale correlation in a pyramid image representation
Machine Vision and Applications
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Image and Vision Computing
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
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Machine Graphics & Vision International Journal
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Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
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Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
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ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Evaluation of Gradient Vector Flow for Interest Point Detection
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Image-Based Information Guide on Mobile Devices
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International Journal of Computer Vision
International Journal of Robotics Research
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PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
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Pattern Recognition Letters
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Image and Vision Computing
Recognition of Plain Objects Using Local Region Matching
IEICE - Transactions on Information and Systems
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Feature Based Affine Invariant Watermarking Robust to Geometric Distortions
Fundamenta Informaticae
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Computers in Biology and Medicine
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Pattern Recognition Letters
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Image and Vision Computing
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Segmentation of Neural Stem/Progenitor Cells Nuclei within 3-D Neurospheres
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Machine Vision and Applications
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Pattern Recognition Letters
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International Journal of Computer Vision
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Computer Vision and Image Understanding
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Computer Vision and Image Understanding
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This paper presents a novel approach for detecting affine invariant interest points. Our method can deal with significant affine transformations including large scale changes. Such transformations introduce significant changes in the point location as well as in the scale and the shape of the neighbourhood of an interest point. Our approach allows to solve for these problems simultaneously. It is based on three key ideas : 1) The second moment matrix computed in a point can be used to normalize a region in an affine invariant way (skew and stretch). 2) The scale of the local structure is indicated by local extrema of normalized derivatives over scale. 3) An affine-adapted Harris detector determines the location of interest points. A multi-scale version of this detector is used for initialization. An iterative algorithm then modifies location, scale and neighbourhood of each point and converges to affine invariant points. For matching and recognition, the image is characterized by a set of affine invariant points; the affine transformation associated with each point allows the computation of an affine invariant descriptor which is also invariant to affine illumination changes. A quantitative comparison of our detector with existing ones shows a significant improvement in the presence of large affine deformations. Experimental results for wide baseline matching show an excellent performance in the presence of large perspective transformations including significant scale changes. Results for recognition are very good for a database with more than 5000 images.