A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface reconstruction with anisotropic density-scaled alpha shapes
Proceedings of the conference on Visualization '98
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
A topological sampling theorem for Robust boundary reconstruction and image segmentation
Discrete Applied Mathematics
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Computer Aided Geometric Design - Special issue: Applications of geometric modeling in the life sciences
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
On the shape of a set of points in the plane
IEEE Transactions on Information Theory
Building the Component Tree in Quasi-Linear Time
IEEE Transactions on Image Processing
BRISK: Binary Robust invariant scalable keypoints
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
The medial feature detector: Stable regions from image boundaries
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
SPBG'05 Proceedings of the Second Eurographics / IEEE VGTC conference on Point-Based Graphics
| Hi-index | 0.00 |
Depending on the application, local feature detectors should comply with properties that are often contradictory, e.g. distinctiveness vs. robustness. Providing a good balance is a standing problem in the field. In this direction, we propose a novel approach for local feature detection starting from sampled edges. The detector is based on shape stability measures across the weighted α-filtration, a computational geometry construction that captures the shape of a non-uniform set of points. The extracted features are blob-like and include non-extremal regions as well as regions determined by cavities of boundary shape. Overall, the approach provides distinctive regions, while achieving high robustness in terms of repeatability and matching score, as well as competitive performance in a large scale image retrieval application.