Simple Camera Calibration From a Single Image Using Five Points on Two Orthogonal 1-D Objects

Abstract

We propose a simple and practical calibration technique that effectively estimates camera parameters from just five points on two orthogonal 1-D objects, each which has three collinear points, one of which is shared. We derive the basic equations needed to realize camera calibration from just five points observed on a single image that captures the objects. We describe a new camera calibration algorithm that estimates the camera parameters based on the basic equations and optimizes them by the bundle adjustment technique. Our method is validated by both computer simulated data and real images. The results show that the camera parameters yielded by our method are close to those yielded by existing methods. The tests demonstrate that our method is both effective and practical.

1D Infrared Camera Distortion Based on Polynomial Model

“…The (1) is also written as： , and we know that the six unknown elements in projective matrix are dependent [11], which is related to two intrinsic parameters and four extrinsic parameters included two rotations and two translations. We can make use of the constraints above mentioned to solve projective matrix.…”

An adaptively weighted algorithm for camera calibration with 1D objects

“…This process is usually a necessary step in extracting 3D metric information from 2D images in many computer vision applications [1][2][3]. Depending on the dimensions of calibration objects, camera calibration techniques can be roughly classified into four categories: 3D calibration [4], 2D calibration [5], 1D calibration [6][7][8][9][10][11][12][13][14][15][16] and 0D calibration or self-calibration [17,18,1]. In self-calibration, only correspondences between image points are used, without any calibration object.…”

“…Wang et al [11] eliminated the motion constraints on a 1D object for multi-camera calibration and proposed a multicamera calibration algorithm with a 1D object undergoing free motion. With some prior knowledge of the camera parameters, Miyagawa et al [12] calibrated a camera with only a single image of two orthogonal 1D objects that shared one point.…”

Visual sensor network lifetime maximization by prioritized scheduling of nodes

“…Therefore, it would be possible to elongate the lifetime of the surveillance through suitable selection of subsets of cooperating sensors. All of the sensors are initially calibrated by an appropriate method (Miyagawa et al, 2010). Hence, the location and orientation of each sensor are known to a base station.…”