Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Geometric Camera Calibration Using Circular Control Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Statistical Bias of Conic Fitting and Renormalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Review of Nonlinear Diffusion Filtering
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
A Four-step Camera Calibration Procedure with Implicit Image Correction
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Smart Nonlinear Diffusion: A Probabilistic Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Reliable Automatic Calibration of a Marker-Based Position Tracking System
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Feature Uncertainty Arising from Covariant Image Noise
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
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Accurate feature detection and localization is fundamentally important to computer vision, and feature locations act as input to many algorithms including camera calibration, structure recovery, and motion estimation. Unfortunately, feature localizers in common use are typically not projectively invariant even in the idealized case of a continuous image. This results in feature location estimates that contain bias which can influence the higher level algorithms that make use of them. While this behavior has been studied in the case of ellipse centroids and then used in a practical calibration algorithm, those results do not trivially generalize to the center-of-mass of a radially symmetric intensity distribution. This paper introduces the generalized result of feature location bias with respect to perspective distortion and applies it to several specific radially symmetric intensity distributions. The impact on calibration is then evaluated. Finally, an initial study is conducted comparing calibration results obtained using center-of-mass to those obtained with an ellipse detector. Results demonstrate that feature localization error, over a range of increasingly large projective distortions, can be stabilized at less than a tenth of a pixel versus errors that can grow to larger than a pixel in the uncorrected case.