1. The internal parameters of the camera are related to the characteristics of the camera itself, such as the focal length and pixel size of the camera.
The internal parameter matrix of the camera reflects the properties of the camera itself, and each camera is different. These parameters need to be calibrated to know. Function: Tell us how the points of camera coordinates continue to pass through the camera lens on the basis of 1 and become pixel points through pinhole imaging and electronic conversion.
Camera internal matrix: (it should be noted that the real lens will also have radial and tangential distortion, which belongs to the camera internal parameters)
fx？ s？ x0
? K =？ 0? Y0 fiscal year
0? 0? 1
Where fx and fy are focal lengths, which are usually equal, x0 and y0 are principal point coordinates (relative to the imaging plane), and S is the coordinate axis tilt parameter, ideally 0.
Parameter meaning of internal parameter matrix:
F: focal length, in millimeters.
Dx: the width of a pixel in the X direction, in millimeters, and how many pixels are there in the X direction of 1/DX: 1mm (DX and dy are the length and height of pixels).
F/dx: Use pixels to describe the length of the focal length in the X-axis direction.
F/dy: pixels are used to describe the length of the focal length in the Y axis direction.
U0, v0, the actual position of the principal point, and the unit is also pixels (translation of the origin).
2. The external parameters of the camera are parameters in the world coordinate system, such as the position and rotation direction of the camera. Compared with the constant internal reference, the external reference changes as the camera moves.
The rotation and translation of the camera is an external parameter, which is used to describe the motion of the camera in a still scene or the rigid motion of a moving object when the camera is fixed. Therefore, in image mosaic or three-dimensional reconstruction, it is necessary to use external parameters to find the relative motion between several images, so as to register in the same coordinate system. Recently, I am studying the image mosaic of multiple images.
Camera external parameter matrix: including rotation matrix and translation matrix. Function: Tell us how a point in the real world (world coordinates) rotates and translates, and then falls on another point in the real world (camera coordinates).
The rotation matrix and translation matrix * * * describe how to transform points from the world coordinate system to the camera coordinate system.
Rotation matrix: describes the direction of the coordinate axis of the world coordinate system relative to the camera coordinate axis.
Translation matrix: describes the position of the spatial origin in the camera coordinate system.
Camera external matrix:
Where r is the rotation matrix and t is the translation vector.
3. Camera calibration (or camera calibration): In a word, it is the mapping from world coordinates to pixel coordinates, in which the world coordinates are artificially defined.
The purpose of camera calibration is to determine the values of some parameters of the camera. Usually these parameters can establish the mapping relationship between the three-dimensional coordinate system determined by the calibration board and the camera image coordinate system. In other words? You can use these parameters to map points in 3D space to image space, and vice versa. The parameters of camera calibration are usually divided into two parts: internal parameters and external parameters.
Calibration is to know the world coordinates and pixel coordinates of calibration control points, so that this mapping relationship can be solved. Once this relationship is solved, we can deduce the world coordinates of a point from its pixel coordinates. Of course, with this world coordinate, we can carry out other follow-up operations such as measurement. The above calibration is also called implicit parameter calibration, because it does not separately calculate the internal parameters of the camera, such as camera focal length, camera distortion coefficient and so on.
Generally speaking, if only some simple visual measurements are made by camera calibration, it is not necessary to calibrate the internal parameters of the camera separately. As for how to calculate the internal parameters of the camera, there are many related papers.
In the process of image measurement and machine vision application, in order to determine the relationship between the three-dimensional geometric position of a point on the surface of a space object and its corresponding point in the image, it is necessary to establish a geometric model of camera imaging, and these geometric model parameters are camera parameters. In most cases, these parameters must be obtained through experiments and calculations. The process of solving these parameters is called camera calibration.
Whether in image measurement or machine vision application, the calibration of camera parameters is a very key link, and the accuracy of calibration results and the stability of the algorithm directly affect the accuracy of the results produced by camera work. Therefore, camera calibration is the premise of follow-up work, and improving calibration accuracy is the focus of scientific research.
4. Distortion is the deviation from straight line projection. Simply put, straight line projection is a straight line in the scene, and it remains a straight line when projected on the screen. Distortion is simply that a straight line cannot be kept as a straight line when it is projected on the screen. This is the optical aberration, which may be the cause of the camera lens.
? Distortion matrix: Tell us why the pixel does not fall in the position where it should fall in theoretical calculation, and there will be some offset and deformation! ! !
Reference link:
Computer Vision-Camera Internal and External Parameters _ Column _ Liulin a603 -CSDN Blog _ Camera Parameters
blogs.com )
Visual odometer of SLAM (2): camera model (internal parameters, external parameters) -Brook _ icv- blog garden (cnblogs.com)