Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A computational approach for corner and vertex detection
International Journal of Computer Vision
Edge, Junction, and Corner Detection Using Color Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Photographing long scenes with multi-viewpoint panoramas
ACM SIGGRAPH 2006 Papers
Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Multiple Object Class Detection with a Generative Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Content Based Image Retrieval System for Multi Object Images Using Combined Features
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
Rover visual obstacle avoidance
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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Significance and usefulness of local invariant features and traditional corner-like features have been widely proven in the literature. In this paper, we novelly combine the two types of features to select salient keypoints with the invariant and corner-like properties, which are highly distinctive and improving match performance. We use moment-derived complex image patterns (e.g., corner, T-junction, sectional cut, and chess-cross) to find corner-like features. We further optimize the matching results by finding corner-like patterns in the invariant matched point correspondences; and rebuff point correspondences that have dissimilar pattern responses which are most likely false matches.