A fully automated geometric lens distortion correction method

Date of Award


Degree Name

M.S. in Electrical Engineering


Department of Electrical and Computer Engineering


Advisor: Eric Balster


In applications such as computer vision and robotics, camera calibration is required to correct geometric lens distortion of images. The problem with most techniques is that they require human involvement in the calibration process. This thesis proposes a new algorithm for camera calibration with no human involvement. Typically in camera calibration process, an image of a calibration target (usually a checkerboard) is acquired for distortion correction. The checkerboard is used because it has known features and is easily segmented. If the image of checkerboard pattern undergoes distortion when the image is captured, and the distortion may be determined by analyzing the image of the checkerboard. The proposed process for coefficient estimation is accomplished by segmenting out the checkerboard of a acquired image. The segmentation is done by finding the connected pixels (components), labeling the connected components and filtering out the unnecessary components from the acquired image. Then the algorithm uses sobel edge detection to detect the vertical and horizontal edges of the checkerboard, because the lines can be used to measure the displacement of image coordinates from their ideal location. Next, the proposed distortion-correction model is applied to the edges of the image with a set of correction coefficients, resulting a set of corrected images. Next the best fit line (synthesized line) is found for each observed line in the each corrected image, and the squared distance between each synthesized and observed line is calculated in each corrected image. The average squared distance is then calculated for each corrected image. Finally, the minimum average distance is found for a set of corrected images in order to obtain the respective image correction coefficients. Both synthetically generated images and natural images have been used to measure the performance of the proposed algorithm. The amount of distortion present in images before and after correction are represented graphically, and results show that the proposed, fully automated algorithm provides equivalent results when compared to other methods which require human involvement.


Cameras Calibration Automation, Photographic lenses Calibration Automation, Optical instruments Calibration Automation

Rights Statement

Copyright © 2011, author