ChESS – Quick and robust detection of chess-board features

• An efficient and reliable method for detecting chess-board vertices is proposed.
• The detector is robust to rotation, blur, and poor scene lighting and contrast.
• The method provides a strength and orientation measure for each detected feature.
• The algorithm outperforms processes often used in calibration and structured light.

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.