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Edit|Zeng You.
Introduction. As a basic problem in the field of computer vision, camera calibration technology has important application value in many applications.
Camera calibration refers to the imaging principle and mathematical model of the camera.
It is one of the key technologies for the camera to obtain real spatial information by observing the image coordinate information of the imaging and the three-dimensional spatial coordinates of the current object to calculate the internal parameters and external parameters of the camera.
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Camera calibration technology is also widely used.
It is mainly used in robots.
Visual, 3D reconstruction, augmented reality.
and other fields. There are already a variety of schemes.
The core of the algorithm for calculating camera parameters is to establish a mathematical model to map the two-dimensional image coordinates with the three-dimensional actual physical space, so as to use traditional computer vision methods for calibration.
Commonly used camera calibration methods, mainly including:
Based on a parametric approach.
And. 
Based on a non-parametric approach.
There are two kinds of parameterization-based methods, including corner point calibration, round point projection calibration, dual-objective calibration and multi-objective calibration.
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Camera calibration technology as a field of computer vision.
Basic technology. Its research and exploration are of great significance for the realization of applications in the fields of machine vision, 3D reconstruction, and augmented reality.
Basics of camera calibration.
Camera calibration refers to the process of determining the internal and external parameters of a camera. Calibrating the camera can obtain the internal parameters (such as focal length, principal point) and external parameters (such as the position and orientation of the camera), so that the image information obtained from the camera can be obtained
Accurately translates to real-world location information.
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The principle of camera imaging refers to the fact that when the incident light is directed at the camera's image sensor (such as CCD CMOS), the camera will record information about the scene and form a digital image. Specifically, the light passes through the lens and enters the camera, where it is focused into a clear image through the camera's optical path system, which is then recorded by the image sensor.
In digital cameras, light passing through the lens and entering the camera is recorded by a CCD CMOS sensor. The image sensor is made up of a light-sensitive unit made up of each pixel. As light passes through the lens and hits the sensor, an electric charge is generated on each pixel, which is converted into a digital image.
Among them, the higher the number of pixels, the higher the clarity of the image.
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Camera Imaging Principle.
It is the process by which the camera records the scene information and forms a digital image. Camera calibration is designed to accurately convert the image information obtained from the camera into real-world position information.
Camera internal parameters refer to the internal parameters of the camera, including focal length, principal point, distortion, and other parameters.
When the camera is placed in a certain situation.
The value of the camera parameter does not change and can be used as a camera calibration parameter.
Camera internal parameters can be provided by the camera manufacturer.
It is also possible to pass.
Camera calibration. and other methods for determination.
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Camera extrinsic parameters refer to the position and orientation of the camera in the world coordinate system, such as the camera's.
Position, orientation, angle of inclination.
Wait. The camera external parameters determine the angle of view and projection relationship of the image when the camera is imaging. Camera exogenous parameters can be used by computer vision methods such as:
3D reconstruction. can also be obtained from sensors such as GPS or IMU.
Application of camera internal and external parameters.
3D Reconstruction: Reconstruct a 3D model by processing 2D images captured by multiple cameras to estimate camera extrinsic and intrinsic parameters.
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Stereo vision refers to the use of contrast measurement and color lens mapping to extract depth information from binocular images, while visual SLAM is based on camera internal and external parameters to construct scene features and location information with reference to scene features and location information.
Photo correction refers to the use of computer vision technology to correct the internal and external parameters of the camera to make the imaging more accurate and beautiful.
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Camera internal and external parameters are important computer vision concepts.
Mastering these concepts has a wide range of applications in 3D reconstruction, stereo vision, visual mapping and image correction.
A video camera is an electronic device that is widely used in fields such as shooting, film production, and television program production.
It converts the picture into an electrical signal through the principle of optical imaging
for subsequent processing and storage.
During the imaging process of the camera, sometimes the problem of image distortion occurs, and distortion correction is required.
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Distortion refers to the error of the camera lens during the imaging process.
Distorts or distorts the image. In camera imaging, there are two main types of distortion, which are:
Radial and tangential distortion.
Radial distortion refers to the formation of inconsistent pixel sizes at the center point and edge of the lens during imaging of the camera lens.
barrel distortion" or "pincushion distortion".
and other shapes. Tangential distortion refers to the distortion of the camera lens during imaging, resulting in the deformation of straight lines, which is called "bending distortion".
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In software distortion correction, the image is mathematically converted and mapped by image processing algorithms to achieve distortion correction.
For example, by analyzing the principle of distortion, the image correction algorithm is used to process the image and transform the image of imaging distortion into the form of a normal image.
This method has the characteristics of low production cost and high processing efficiency, and is widely used.
In hardware distortion correction, the distortion during imaging is minimized or eliminated by designing and assembling the lens.
This approach typically involves firmware and hardware optimization, which needs to be tweaked and implemented in camera manufacturing.
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Camera calibration method.
Camera calibration refers to the three-dimensional coordinates of the images collected by the computer in the real world under the premise of camera imaging. The implementation of camera calibration requires the measurement and calculation of imaging parameters by multiple ** or **.
In the template matching method.
We do this by making a sheet with a specific mark.
Template diagram. It is placed in the calibration scene before the camera, and the image is matched and analyzed by the software with the image acquired by the camera.
In turn, key parameters such as the built-in parameters of the camera coordinate system and the transformation matrix are inverted.
The template matching method is mainly divided into two stages:
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Before the camera imaging, the marked template map is placed in the required calibration space, and a plurality of images of different angles and distances are taken;
through image processing technology.
The feature point detection is carried out on the template map captured by the camera to obtain the coordinates of the template map in the image coordinate system.
According to the feature point detection of the template diagram at different angles captured by the camera, the camera internal reference matrix and the external parameter matrix in different states are obtained, and the transformation matrix is calculated.
The obtained calibration parameters are used for subsequent image processing.
and the new images taken.
Standardized corrections.
and other operations to realize the mapping calculation from the camera imaging coordinate system to the world coordinate system.
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The template matching method is one.
Convenient and reliable.
The camera calibration method. However, this method also has some limitations, such as the production of the template diagram and the recalibration of the marker template diagram when it is changed, which affects the convenience and practicability of the operation.
A corner is a point of special detail in an image that has:
High sensitivity and robustness.
This is called the important feature point of the image. In the fields of image processing, computer vision, and machine vision, the corners are in the image.
Matching, stitching, tracking, and 3D reconstruction.
and so on.
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The process of point position, where the more common method is the " in the corner extraction algorithm
Corner Response Function".
filter。
The response function of a corner is to slide a moving window on the image and calculate the change of the gray value of the window in different directions, usually using the Harris corner detection algorithm to detect the corners in the image.
The Harris corner detection algorithm was proposed by Christharris and Mikestephens in 1988. The purpose of this algorithm is to identify the corners in the image by calculating the color value of the pixel in the image, the gradient change of two adjacent pixels.
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Corner extraction algorithm.
It is a widely used technology in the field of image processing and computer vision, which can help identify important feature points of images, thereby effectively improving the understanding and analysis of image content.
In practical applications, more accurate and reliable corner extraction can be achieved by selecting appropriate corner detection algorithms and parameters according to specific scenarios and application requirements.
Direct method. It is a method commonly used in the field of computer vision, which is a method based on geometric constraints and can be used in a variety of applications, such as camera calibration, positioning, and tracking. Unlike other methods, the direct method does not require feature extraction or feature matching, but is calculated directly based on the pixel value or the position of the point.
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In the direct method, a continuous sequence of images of the observed scene is used to infer motion and scene information.
The motion of a visual odometry is calculated by comparing the pixel values of an image, a technique known as optical flow-based motion estimation.
You can also establish some by restricting how adjacent images are matched to each other.
Geometric constraints. Such as baseline constraints, plane constraints, and trajectory constraints.
The advantage is that it can handle issues such as grayscale variations and texture blurring.
It is also capable of handling rapid movements and scale changes. In real-time applications, the calculation speed of the direct method is often faster, because it does not need to extract and match feature points, and directly calculates with continuous image sequences.
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However, the direct method is more sensitive to noise and illusions.
In the case of a large sequence of consecutive images, the computational complexity using the direct method is also relatively large. Due to the direct method of pixel-by-pixel calculation, the motion vector may be less stable and require further optimization and smoothing.
Camera calibration applications.
Camera correction and image registration, the internal and external parameters of the camera can be obtained through camera calibration.
to achieve dedistortion and registration of images. For medicine.
Imaging, tracking and tracking.
and other fields.
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Visual measurement and 3D reconstruction.
Through camera calibration, the distance relationship between the pixels in the image and the actual scene can be accurately obtained, and then the three-dimensional reconstruction of the actual scene can be realized.
In addition, the camera is calibrated.
It is also the basis for the implementation of algorithms such as feature point tracking, optical flow calculation, and stereo vision in computer vision.
Camera pose estimation refers to the rotation vector and translation vector of the camera that can be obtained through camera calibration.
Pose estimation of the camera can be realized. In the field of industrial manufacturing, it is often used in scenarios such as robot operation and UAV flight attitude estimation.
Conclusion. Camera calibration technology is in.
Computer vision, robot vision, 3D modeling, and augmented reality.
It has a wide range of applications in other fields. Through camera calibration, we can obtain various parameters of the camera, which can solve problems such as image processing, scale calculation and 3D reconstruction.
More accurate and reliable.
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With the continuous development and popularization of computer technology, camera calibration technology will be further popularized and applied in the future.
At the same time, it is necessary to further study the new camera parameter calibration method.
For example, the camera calibration method based on deep learning can improve the quality and efficiency of calibration, help expand application scenarios, and promote the development of related fields.
In the future, camera calibration technology will be further popularized and applied in the continuous development. The camera calibration method based on deep learning will become an important development direction of camera calibration technology.
The wide application of deep learning provides new ideas for camera calibration technology.
Through deep learning, we can locate and recognize the images captured by the camera more accurately and efficiently, and can automatically calibrate and correct them during image processing.
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