Camera Calibration in Computer Vision

Camera Calibration:

Geometric Camera Calibration also referred to as Camera Re-sectioning. It estimates the parameters of a less and image sensor of an image or video camera. You can use these parameters to correct for lens distortion. It measures the size of an object in world units or determine the location of the camera in the scene. These tasks are used in application such as machine vision to detect and measure objects. They are also used in robotics for navigation systems and 3D scene reconstruction.

Camera parameters include intrinsic, extrinsic and distortion coefficients. To estimate the camera parameters, you need to have 3D world points and their corresponding 2D image points. You can get these correspondences using multiple images of a calibration pattern, such as a checkerboard. Using the correspondences, you can solve for the camera parameters. After you calibrate a camera to evaluate the accuracy of the estimated parameters, you can:

• Plot the relative locations of the camera and the calibration pattern
• Calculate the projection errors
• Calculate the parameter estimation errors

Camera Model:

The Computer Vision Toolbox or calibration algorithm uses the camera model proposed by Jean-Yves Bouguet. The model include:

• The pinhole camera model
• Lens distortion

The pinhole camera model doesn’t account for lens distortion because an ideal pinhole camera doesn’t have a lens. To accurately represent a real camera, the full camera model used by the algorithm includes the radial and tangential lens distortion.

Pinhole Camera Model:

A pinhole camera is a simple camera without a lens and with a single small aperture. Light rays pass through the aperture and project an inverted image on the opposite side of the camera. Think of the virtual image plane as being in front of the camera and containing the upright image of the scene.

The pinhole camera parameters are represented in a 4-by-3 matrix called the camera matrix. This matrix maps the 3D world scene into the image plane. The calibration algorithm calculates the camera matrix using the extrinsic and intrinsic parameters. The extrinsic parameters represent the location of the camera in the 3D scene. The intrinsic parameters represent the optical center and focal length of the camera. The world points are transformed to camera coordinate using the extrinsic parameters. The camera coordinates are mapped into the image plane using the intrinsic parameters.

Camera Calibration Parameters:

The calibration algorithm calculates the camera matrix using the extrinsic and intrinsic parameters. The extrinsic parameters represent a rigid transformation from 3D world coordinate system to the 3D camera coordinate system. The intrinsic parameters represent a protective transformation from the 3D camera coordinates into the 2D image coordinates.

i. Extrinsic Parameters: The extrinsic parameters consist of a rotation, R and a translation, t. The origin of the camera’s coordinate system is at its optical center and its x and y-axis define the image plane.

ii. Intrinsic Parameters: The intrinsic parameters include the focal length, the optical center also known as the principal point and the skew coefficient.

Distortion in Camera Calibration:

The camera matrix doesn’t account for lens distortion because an ideal pinhole camera doesn’t have a lens. To accurately represent a real camera, the camera model includes the radial and tangential lens distortion.

i. Radial Distortion: It occurs when light rays bend more near the edges of a lens than they do at its optical center. The smaller the lens, the greater the distortion.

ii. Tangential Distortion: It occurs when the lens and the image plane are not parallel. The tangential distortion coefficients model this type of distortion.