Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Signal Processing for Computer Vision
Signal Processing for Computer Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Patch-Duplets for Object Recognition and Pose Estimation
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Features for Object Class Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Autonomous Learning of Object Appearances using Colour Contour Frames
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Evaluation of Features Detectors and Descriptors based on 3D Objects
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
An invariant and compact representation for unrestricted pose estimation
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Local single-patch features for pose estimation using the log-polar transform
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Incremental computation of feature hierarchies
Proceedings of the 32nd DAGM conference on Pattern recognition
Multiple viewpoint recognition and localization
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Methodology for evaluating static six-degree-of-freedom (6DoF) perception systems
Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
Robust point matching revisited: a concave optimization approach
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Motion planning efficient trajectories for industrial bin-picking
International Journal of Robotics Research
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Recent years have seen advances in the estimation of full 6 degree-of-freedom object pose from a single 2D image. These advances have often been presented as a result of, or together with, a new local image descriptor. This paper examines how the performance for such a system varies with choice of local descriptor. This is done by comparing the performance of a full 6 degree-of-freedom pose estimation system for fourteen types of local descriptors. The evaluation is done on a database with photos of complex objects with simple and complex backgrounds and varying lighting conditions. From the experiments we can conclude that duplet features, that use pairs of interest points, improve pose estimation accuracy, and that affine covariant features do not work well in current pose estimation frameworks. The data sets and their ground truth is available on the web to allow future comparison with novel algorithms.