Authors:
Zoltán Pusztai
1
and
Levente Hajder
2
Affiliations:
1
Geometric Computer Vision Group, Machine Perception Laboratory, MTA SZTAKI, Kende st. 17, Budapest 1111, Hungary, Department of Algorithm and Applications, Eötvös Loránd University, Pázmány Péter stny. 1/C, Budapest 1117 and Hungary
;
2
Department of Algorithm and Applications, Eötvös Loránd University, Pázmány Péter stny. 1/C, Budapest 1117 and Hungary
Keyword(s):
Feature detector, Quantitative Comparison, Affine Transformation, Detection Error, Ground Truth Generation.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Optical Flow and Motion Analyses
;
Shape Representation and Matching
;
Stereo Vision and Structure from Motion
;
Tracking and Visual Navigation
Abstract:
Feature detectors are frequently used in computer vision. Recently, detectors which can extract the affine transformation between the features have become popular. With affine transformations, it is possible to estimate the properties of the camera motion and the 3D scene from significantly fewer feature correspondences. This paper quantitatively compares the affine feature detectors on real-world images captured by a quadcopter. The ground truth (GT) data are calculated from the constrained motion of the cameras. Accurate and very realistic testing data are generated for both the feature locations and the corresponding affine transformations. Based on the generated GT data, many popular affine feature detectors are quantitatively compared.