Camera resectioning is the process of estimating the parameters of a pinhole camera model approximating the camera that produced a given photograph or video; it determines which incoming light ray is associated with each pixel on the resulting image.
Basically, the process determines the pose of the pinhole camera.
The extrinsic parameters define the camera pose (position and orientation) while the intrinsic parameters specify the camera image format (focal length, pixel size, and image origin).
The camera projection matrix is derived from the intrinsic and extrinsic parameters of the camera, and is often represented by the series of transformations; e.g., a matrix of camera intrinsic parameters, a 3 × 3 rotation matrix, and a translation vector.
In both cases, they are represented in homogeneous coordinates (i.e. they have an additional last component, which is initially, by convention, a 1), which is the most common notation in robotics and rigid body transforms.
are the coordinates of the source of the light ray which hits the camera sensor in world coordinates, relative to the origin of the world.
These parameters encompass focal length, image sensor format, and camera principal point.
are the inverses of the width and height of a pixel on the projection plane and
represent the principal point, which would be ideally in the center of the image.
Nonlinear intrinsic parameters such as lens distortion are also important although they cannot be included in the linear camera model described by the intrinsic parameter matrix.
Many modern camera calibration algorithms estimate these intrinsic parameters as well in the form of non-linear optimisation techniques.
Camera calibration is often used as an early stage in computer vision.
When a camera is used, light from the environment is focused on an image plane and captured.
This process reduces the dimensions of the data taken in by the camera from three to two (light from a 3D scene is stored on a 2D image).
Each pixel on the image plane therefore corresponds to a shaft of light from the original scene.
There are many different approaches to calculate the intrinsic and extrinsic parameters for a specific camera setup.
between the calibration target and the image plane is determined using DLT method.
[4] Subsequently, self-calibration techniques are applied to obtain the image of the absolute conic matrix.
poses of the calibration target, extract a constrained intrinsic matrix
of course means they are also projected onto the image of the absolute conic (IAC)
as follows: Tsai's algorithm, a significant method in camera calibration, involves several detailed steps for accurately determining a camera's orientation and position in 3D space.
The procedure, while technical, can be generally broken down into three main stages: The process begins with the initial calibration stage, where a series of images are captured by the camera.
These images, often featuring a known calibration pattern like a checkerboard, are used to estimate intrinsic camera parameters such as focal length and optical center.
[6] In some applications, variants of the chessboard target are used which are robust to partial occlusions.
Following initial calibration, the algorithm undertakes pose estimation.
This involves calculating the camera's position and orientation relative to a known object in the scene.
The process typically requires identifying specific points in the calibration pattern and solving for the camera's rotation and translation vectors.
Further optimization of internal and external camera parameters is performed to enhance the calibration accuracy.
This structured approach has positioned Tsai's Algorithm as a pivotal technique in both academic research and practical applications within robotics and industrial metrology.
Intensity based registration based on an arbitrary X-ray image and a reference model (as a tomographic dataset) can then be used to determine the relative camera parameters without the need of a special calibration body or any ground-truth data.