A computational approach for corner and vertex detection
International Journal of Computer Vision
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Content-Based Image Retrieval Based on Local Affinely Invariant Regions
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Wavelet-Based Salient Points: Applications to Image Retrieval Using Color and Texture Features
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Zero Phase Representation of Panoramic Images for Image Vased Localization
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Matching Widely Separated Views Based on Affine Invariant Regions
International Journal of Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Towards automatic visual obstacle avoidance
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Ultra-fast tracking based on zero-shift points
Image and Vision Computing
| Hi-index | 0.00 |
Stable Wave Detector (SWD) is a new multiscale landmark detector in the intensity image. SWD belongs to a group of interest-point-like operators aiming at detecting repeatedly distinguished entities regardless of their semantics. The speed and the robustness of landmark detection and the precision of landmark localization are main issues. The target landmarks are blobs which correspond to local maxima/minima of intensity (positive and negative peaks). The detector is based on the phase of the first harmonic wave in the moving window. The localization is a result of an integral transformation rather than a derivative. Thus, the blob detector is inherently robust to noise. The SWD provides subpixel localization of blobs together with the estimate of its precision, the measure of the strength/significance and the estimate of the size/scale for each blob.