Solid shape
Robust Image Corner Detection Through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stability of corner points in scale space: the effects of small nonrigid deformations
Computer Vision and Image Understanding
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Analysis of gray level corner detection
Pattern Recognition Letters
Saliency, Scale and Image Description
International Journal of Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Computer and Robot Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
A representation for visual information with application to machine vision
A representation for visual information with application to machine vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multi-Image Matching Using Multi-Scale Oriented Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Comparison of Affine Region Detectors
International Journal of Computer Vision
A blob detector in color images
Proceedings of the 6th ACM international conference on Image and video retrieval
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
A Scale Invariant Interest Point Detector for Discriminative Blob Detection
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Affine Adaptation of Local Image Features Using the Hessian Matrix
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Performance evaluation of corner detectors using consistency and accuracy measures
Computer Vision and Image Understanding
ASIFT: A New Framework for Fully Affine Invariant Image Comparison
SIAM Journal on Imaging Sciences
Maximally stable local description for scale selection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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Usually, state-of-the-art interest point detectors tend to over-represent the local image structures associating several interest points for each local image structure. This fact avoids interest points to clearly stand out against its neighborhood, losing the ability to clearly describe the global uniqueness of each local image structure. In order to solve this problem we propose a sparse affine invariant blob detector, which tries to describe each blob structure with a single interest point. The proposed detector is carried out in two stages: an initial stage, where a set of scale invariant interest points are located by means of the idea of blob movement and blob evolution (creation, annihilation and merging) along different scales by using a precise description of the image provided by the Gaussian curvature, providing a global bottom-up estimation of the image structure. During the second stage, the shape and location of each scale invariant interest point is refined by fitting an anisotropic Gaussian function, which minimizes the error with the underlying image and simultaneously estimates both the shape and location, by means of a non-linear least squares approach. A comparative evaluation of affine invariant detectors is presented, showing that our approach outperforms state-of-the-art affine invariant detectors in terms of precision and recall, and obtains a similar performance to that of the best ones in terms of repeatability and matching. In addition we demonstrate that our detector does not over-represent blob structures and provides a sparse detection that improves distinctiveness and reduces drastically the computational cost of matching tasks. In order to verify the accuracy and the reduction in the computational cost we have evaluated our detector in image registration tasks.