Simultaneously calibrating catadioptric camera and detecting line features using Hough transform

A line in space is projected to a conic in a central catadioptric image, and such a conic is called a line image. This paper proposes a novel approach to calibrating catadioptric camera and detecting line images simultaneously by using Hough transform. Previous approaches to catadioptric cameras calibration employ the traditional conic detecting or fitting methods for line images, and then use these recovered conies to estimate the intrinsic parameters based on some properties of line images. However, the type of a line image can be line, circle, ellipse, hyperbola or parabola, and in general only a small arc of the conic is visible in the image, which brings novel challenges for conic detection and fitting where traditional conic detecting and fitting methods may fail. As we know, the accuracy of the estimated intrinsic parameters highly depends on the accuracy of the extracted conies. The main contribution of this work is we show that all line images from catadioptric cameras with the same intrinsic parameters must belong to a family of conies with only two degree-of-freedom, and such a family is called a line image family. Therefore, we present a novel special Hough transform for line image detection which ensures that all detected conies must belong to a line image family related to certain intrinsic parameters. For all possible values of the unknown intrinsic parameters, the line image special Hough transform are performed. The one with the highest confidence is chosen as the estimated values for these unknown intrinsic parameters, and the corresponding results of line image detection are chosen as the estimated values for line images. In order to make the searching process more efficient, the hierarchical approaches are employed in this paper. The validity of our proposed approach is illustrated by experiments.