Calibration and Rectification Research for Fish-eye lens Application

The purpose of this paper aims to promote the application of fish-eye lens. Accurate parameters calibration and effective distortion rectification of an imaging device is of utmost importance in machine vision. Fish-eye lens produces a hemispherical field of view of an environment, which appears definite significant since its advantage of panoramic sight with a single compact visual scene. But fish-eye lens image has an unavoidable inherent severe distortion. The precise optical center is the precondition for other parameters calibration and distortion correction. Therefore, three different optical center calibration methods have been researched for diverse applications. Support Vector Machine (SVM) and Spherical Equidistance Projection Algorithm (SEPA) are integrated to replace traditional rectification methods. SVM is a machine learning method based on the theory of statistics, which have good capabilities of imitating, regression and classification. In this research, SVM provides a mapping table between the fish-eye image and the standard image for human eyes. Two novel training models have been designed. SEPA has been applied to promote the rectification effect of the edge of fish-eye lens image. The validity and effectiveness of our achievements are demonstrated by processing the real images.