Bennett, S and Lasenby, J (2014) ChESS - Quick and robust detection of chess-board features. Computer Vision and Image Understanding, 118. pp. 197-210. ISSN 1077-3142Full text not available from this repository.
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. © 2013 Elsevier Inc. All rights reserved.
|Uncontrolled Keywords:||Camera calibration Chess-board corner detection Feature extraction Pattern recognition Photogrammetric marker detection Structured light surface measurement|
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Cron job|
|Date Deposited:||16 Jul 2015 13:19|
|Last Modified:||03 Aug 2015 09:17|