Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
An Efficient Solution to the Five-Point Relative Pose Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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
Coding Images with Local Features
International Journal of Computer Vision
Measuring the coverage of interest point detectors
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Hi-index | 0.00 |
We investigate the suitability of different local feature detectors for the task of automatic image orientation under different scene texturings. Building on an existing system for image orientation, we vary the applied operators while keeping the strategy fixed, and evaluate the results. An emphasis is put on the effect of combining detectors for calibrating difficult datasets. Besides some of the most popular scale and affine invariant detectors available, we include two recently proposed operators in the setup: A scale invariant junction detector and a scale invariant detector based on the local entropy of image patches. After describing the system, we present a detailed performance analysis of the different operators on a number of image datasets. We both analyze ground-truth-deviations and results of a final bundle adjustment, including observations, 3D object points and camera poses. The paper concludes with hints on the suitability of the different combinations of detectors, and an assessment of the potential of such automatic orientation procedures.