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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. ==Chessboard camera calibration== 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.〔D. Forsyth and J. Ponce. Computer Vision: A Modern Approach. Prentice Hall. (2002). (ISBN 978-0262061582 ).〕 Practical cameras are complex devices, and photogrammetry is needed to model the relationship between image sensor measurements and the 3D world. In the standard pinhole camera model, one models the relationship between world coordinates and image (pixel) coordinates via the perspective transformation : where is the projective space of dimension . In this setting, camera calibration is the process of estimating the parameters of the matrix of the perspective model. Camera calibration is an important step in the computer vision pipeline because many subsequent algorithms require knowledge of camera parameters as input.〔R. Szeliski. Computer Vision: Algorithms and Applications. Springer Science and Business Media. (2010). (ISBN 978-1848829350 ).〕 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. The following two methods are classic calibration techniques that often employ chessboards. 抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「Chessboard detection」の詳細全文を読む スポンサード リンク
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