A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusing Points and Lines for High Performance Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
PROCAMS '08 Proceedings of the 5th ACM/IEEE International Workshop on Projector camera systems
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
A Multiscale Sub-pixel Detector for Corners in Camera Calibration Targets
ICICTA '10 Proceedings of the 2010 International Conference on Intelligent Computation Technology and Automation - Volume 01
A Robust Recognition Technique for Dense Checkerboard Patterns
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Accurate chequerboard corner localisation for camera calibration
Pattern Recognition Letters
Simulation of breathing for medical applications
ACM SIGGRAPH 2011 Posters
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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Localization of chess-board vertices is a common task in computer vision, underpinning many applications, but relatively little work focusses on designing a specific feature detector that is fast, accurate and robust. In this paper the 'Chess-board Extraction by Subtraction and Summation' (ChESS) feature detector, designed to exclusively respond to chess-board vertices, is presented. The method proposed is robust against noise, poor lighting and poor contrast, requires no prior knowledge of the extent of the chess-board pattern, is computationally very efficient, and provides a strength measure of detected features. Such a detector has significant application both in the key field of camera calibration, as well as in structured light 3D reconstruction. Evidence is presented showing its superior robustness, accuracy, and efficiency in comparison to other commonly used detectors, including Harris & Stephens and SUSAN, both under simulation and in experimental 3D reconstruction of flat plate and cylindrical objects.