Conic Reconstruction and Correspondence From Two Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Geometry and Matching of Lines and Curves Over Multiple Views
International Journal of Computer Vision
On Calibration and Reconstruction from Planar Curves
ECCV '00 Proceedings of the 6th 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
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Quadric Reconstruction from Dual-Space Geometry
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Multiple View Geometry of General Algebraic Curves
International Journal of Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Camera Calibration with One-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Time Maximally Stable Extremal Regions
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Projective reconstruction of ellipses from multiple images
Pattern Recognition
Situation analysis and atypical event detection with multiple cameras and multi-object tracking
RobVis'08 Proceedings of the 2nd international conference on Robot vision
Traffic observation and situation assessment
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
| Hi-index | 0.00 |
The classic feature matching process has two drawbacks. Firstly, ambiguous but possibly correct matches will potentially be removed and secondly, there is no constraint for the 2D size of the features. In the present paper these drawbacks are tackled at once with a different approach: by considering region features instead of point features and by adding constraints based on the features' shape. Here, the shape will be described with an ellipse. Using existing knowledge about the algebraic properties of ellipses within the computer vision domain, this enables additional constraints such as ellipse tangents. The number of ambiguous matches is reduced and increased control of the physical 2D size of the features is obtained. This will be shown on known epipolar geometry. Additionally, reconstruction of feature ellipses is examined.