Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing corners by fitting parametric models
International Journal of Computer Vision
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Signal Processing for Computer Vision
Signal Processing for Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Visual methods for three-dimensional modeling
Visual methods for three-dimensional modeling
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Generic Camera Model and Calibration Method for Conventional, Wide-Angle, and Fish-Eye Lenses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design of steerable filters for feature detection using canny-like criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of Superimposed Oriented Patterns
IEEE Transactions on Image Processing
Analysis of length and orientation of microtubules in wide-field fluorescence microscopy
Proceedings of the 32nd DAGM conference on Pattern recognition
A depth cue method based on blurring effect in augmented reality
Proceedings of the 4th Augmented Human International Conference
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We describe a technique to detect and localize features on checkerboard calibration charts with high accuracy. Our approach is based on a model representing the sought features by a multiplicative combination of two edge functions, which, to allow for perspective distortions, can be arbitrarily oriented. First, candidate regions are identified by an eigenvalue analysis of the structure tensor. Within these regions, the sought checkerboard features are then detected by matched filtering. To efficiently account for the double-oriented nature of the sought features, we develop an extended version of steerable filters, viz., multi-steerable filters. The design of our filters is carried out by a Fourier series approximation. Multisteerable filtering provides both the unknown orientations and the positions of the checkerboard features, the latter with pixel accuracy. In the last step, the feature positions are refined to subpixel accuracy by fitting a paraboloid. Rigorous comparisons show that our approach outperforms existing feature localization algorithms by a factor of about three.