3DPO: A three-dimensional part orientation system
International Journal of Robotics Research
The representation, recognition, and locating of 3-d objects
International Journal of Robotics Research
Representation of local geometry in the visual system
Biological Cybernetics
Readings in computer vision: issues, problems, principles, and paradigms
Stereo Correspondence Through Feature Grouping and Maximal Cliques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Finding the collineation between two projective reconstructions
Computer Vision and Image Understanding
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Vision-Based SLAM: Stereo and Monocular Approaches
International Journal of Computer Vision
Registration of Challenging Image Pairs: Initialization, Estimation, and Decision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized least squares-based parametric motion estimation
Computer Vision and Image Understanding
Fast construction of dynamic and multi-resolution 360° panoramas from video sequences
Image and Vision Computing
EURASIP Journal on Advances in Signal Processing
Vision-based absolute navigation for descent and landing
Journal of Field Robotics
Keypoints and Local Descriptors of Scalar Functions on 2D Manifolds
International Journal of Computer Vision
Multimedia Tools and Applications
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In this paper we address the problem of matching two images with two different resolutions: a high-resolution image and a low-resolution one. The difference in resolution between the two images is not known and without loss of generality one of the images is assumed to be the high-resolution one. On the premise that changes in resolution act as a smoothing equivalent to changes in scale, a scale-space representation of the high-resolution image is produced. Hence the one-to-one classical image matching paradigm becomes one-to-many because the low-resolution image is compared with all the scale-space representations of the high-resolution one. Key to the success of such a process is the proper representation of the features to be matched in scale-space. We show how to represent and extract interest points at variable scales and we devise a method allowing the comparison of two images at two different resolutions. The method comprises the use of photometric- and rotation-invariant descriptors, a geometric model mapping the high-resolution image onto a low-resolution image region, and an image matching strategy based on local constraints and on the robust estimation of this geometric model. Extensive experiments show that our matching method can be used for scale changes up to a factor of 6.