A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Local Grayvalue Invariants for Image Retrieval
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
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Branch Points in One-Dimensional Gaussian Scale Space
Journal of Mathematical Imaging and Vision
Stochastic models for generic images
Quarterly of Applied Mathematics
On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
Statistical Edge Detection: Learning and Evaluating Edge Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Distance Measure and a Feature Likelihood Map Concept for Scale-Invariant Model Matching
International Journal of Computer Vision
A Probabilistic Multi-scale Model for Contour Completion Based on Image Statistics
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Feature Based Methods for Structure and Motion Estimation
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
What Do Features Tell about Images?
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Jet Based Feature Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A novel performance evaluation method of local detectors on non-planar scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Evaluation of Features Detectors and Descriptors based on 3D Objects
International Journal of Computer Vision
Learning Probabilistic Models for Contour Completion in Natural Images
International Journal of Computer Vision
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
Basic Image Features (BIFs) Arising from Approximate Symmetry Type
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
The Representation and Matching of Images Using Top Points
Journal of Mathematical Imaging and Vision
Discriminative Learning of Local Image Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A Representation for Shape Based on Peaks and Ridges in the Difference of Low-Pass Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relighting human locomotion with flowed reflectance fields
EGSR'06 Proceedings of the 17th Eurographics conference on Rendering Techniques
Comparative evaluation of binary features
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Jet-Based local image descriptors
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Towards space-time semantics in two frames
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Adaptive structure from motion with a contrario model estimation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
SIFER: Scale-Invariant Feature Detector with Error Resilience
International Journal of Computer Vision
Evaluation of two-view geometry methods with automatic ground-truth generation
Image and Vision Computing
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Not all interest points are equally interesting. The most valuable interest points lead to optimal performance of the computer vision method in which they are employed. But a measure of this kind will be dependent on the chosen vision application. We propose a more general performance measure based on spatial invariance of interest points under changing acquisition parameters by measuring the spatial recall rate. The scope of this paper is to investigate the performance of a number of existing well-established interest point detection methods. Automatic performance evaluation of interest points is hard because the true correspondence is generally unknown. We overcome this by providing an extensive data set with known spatial correspondence. The data is acquired with a camera mounted on a 6-axis industrial robot providing very accurate camera positioning. Furthermore the scene is scanned with a structured light scanner resulting in precise 3D surface information. In total 60 scenes are depicted ranging from model houses, building material, fruit and vegetables, fabric, printed media and more. Each scene is depicted from 119 camera positions and 19 individual LED illuminations are used for each position. The LED illumination provides the option for artificially relighting the scene from a range of light directions. This data set has given us the ability to systematically evaluate the performance of a number of interest point detectors. The highlights of the conclusions are that the fixed scale Harris corner detector performs overall best followed by the Hessian based detectors and the difference of Gaussian (DoG). The methods based on scale space features have an overall better performance than other methods especially when varying the distance to the scene, where especially FAST corner detector, Edge Based Regions (EBR) and Intensity Based Regions (IBR) have a poor performance. The performance of Maximally Stable Extremal Regions (MSER) is moderate. We observe a relatively large decline in performance with both changes in viewpoint and light direction. Some of our observations support previous findings while others contradict these findings.