IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphical Models and Image Processing
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Robust detection of significant points in multiframe images
Pattern Recognition Letters
Saliency, Scale and Image Description
International Journal of 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
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
Visual mapping by a robot rover
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
A domain reduction algorithm for incremental projective reconstruction
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Hi-index | 0.00 |
In this paper a variant of the Harris corner point detector is introduced. The new algorithm use a covariance operator to compute the angular difference between dominant edges. Then, a new cornerness strength function is proposed by weighting the log Harris cornerness function by the angular difference between dominant edges. An important advantage of the proposed corner detector algorithm is its ability to reduce false corner responses in image regions where partial derivatives have similar values. In addition, we show qualitatively that ranking corner points with the new cornerness strength function better agrees with the intuitive notion of a corner than the original Harris function. To demonstrate the performance of the new algorithm, the new approach is applied on synthetic and real images. The results show that the proposed algorithm rank better the meaningful detected features and at the same time reduces false positive features detected when compared to the original Harris algorithm.