Error analysis of fisheye correction curve

Various types of image can be captured with fisheye lens, their wide field of view is particularly suited to a stereo vision. However, fisheye lens introduces distortion, this change makes the image difficult to identify. If a correction curve can be fitted to the distortion shape, the degree of distortion can be described, support vector machine is a popular machine learning method for classification and regression, support vector classification can find optimal interval of the different classified data, the classified curve is as the correction curve, the training data is obtained by corner detection, the center of the data is gained by Hough transform. Although ignoring the error in the process of the algorithm, but the error is still existed during the picture taken from a fisheye lens in different situation, for example angle or distance, then using the support vector regression to gain the error between the original data and predicted data, original data stem from training target which is also obtained by a fisheye lens and predicted data stem from another fisheye image.