A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Content-Based Image Retrieval Based on Local Affinely Invariant Regions
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
CSDD Features: Center-Surround Distribution Distance for Feature Extraction and Matching
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Hyperfeatures – multilevel local coding for visual recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Hi-index | 0.00 |
We believe that the potential of edges in local feature detection has not been fully exploited and therefore propose a detector that starts from single scale edges and produces reliable and interpretable blob-like regions and groups of regions of arbitrary shape. The detector is based on merging local maxima of the distance transform guided by the gradient strength of the surrounding edges. Repeatability and matching score are evaluated and compared to state-of-the-art detectors on standard benchmarks. Furthermore, we demonstrate the potential application of our method to wide-baseline matching and feature detection in sequences involving human activity.