Chessboard detection

Chessboards arise frequently in computer vision theory and practice because their highly structured geometry is well-suited for algorithmic detection and processing.

The appearance of chessboards in computer vision can be divided into two main areas: camera calibration and feature extraction.

This article provides a unified discussion of the role that chessboards play in the canonical methods from these two areas, including references to the seminal literature, examples, and pointers to software implementations.

A classical problem in computer vision is three-dimensional (3D) reconstruction, where one seeks to infer 3D structure about a scene from two-dimensional (2D) images of it.

[1] Practical cameras are complex devices, and photogrammetry is needed to model the relationship between image sensor measurements and the 3D world.

[2] Chessboards are often used during camera calibration because they are simple to construct, and their planar grid structure defines many natural interest points in an image.

In particular, DLT calibration exploits the fact that the perspective pinhole camera model defines a set of similarity relations that can be solved via the direct linear transformation algorithm.

A common way to achieve this is to construct a camera calibration rig (example below) built from three mutually perpendicular chessboards.

[4] Zhang's method calibrates cameras by solving a particular homogeneous linear system that captures the homographic relationships between multiple perspective views of the same plane.

The following figures demonstrate a practical application of multiplane camera calibration from multiple views of a chessboard.

[5] The second context in which chessboards arise in computer vision is to demonstrate several canonical feature extraction algorithms.

The following sections demonstrate the application of common feature extraction algorithms to a chessboard image.

Corners are a natural local image feature exploited in many computer vision systems.

[7][8][9] As such, one can detect lines in an image by simply searching for local maxima of its discrete Hough transform.

Indeed, the following figure demonstrates Hough transform-based line detection applied to a perspective-transformed chessboard image.

This assumption may be violated e.g. when specular reflections due to inhomogenous lighting cause chessboard detection to fail in some of the corners.

The measurement of camera distortions close to the image corners is also altered by the need of a completely visible chessboard target.

The main advantage of such ChArUco targets[11] is that all light chessboard squares are uniquely coded and identifiable.