ChESS - Quick and Robust Detection of Chess-board Features

TL;DR

The paper introduces ChESS, a fast, accurate, and robust chess-board feature detector designed for computer vision tasks like calibration and 3D reconstruction, outperforming existing methods in noise and lighting conditions.

Contribution

It presents a novel detector specifically for chess-board vertices that is efficient, robust, and does not require prior pattern size knowledge.

Findings

Demonstrates robustness against noise, poor lighting, and low contrast.

Shows high accuracy and efficiency in simulations and real experiments.

Outperforms existing detectors in key applications like camera calibration.

Abstract

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