  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 robustness, accuracy, and efficiency in comparison to
other commonly used detectors both under simulation and in experimental 3D
reconstruction of flat plate and cylindrical objects
