Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy

TL;DR

This paper introduces a robust, self-calibration method for camera distortion correction using single images with straight lines, based on minimizing Hough space entropy, applicable to various image types and distortion models.

Contribution

The method is novel in using entropy minimization in Hough space for distortion correction without relying on edge fitting or specific image structures.

Findings

Effective on synthetic and real data

Robust to noise

Applicable to various distortion models

Abstract

In this paper we present a simple and robust method for self-correction of camera distortion using single images of scenes which contain straight lines. Since the most common distortion can be modelled as radial distortion, we illustrate the method using the Harris radial distortion model, but the method is applicable to any distortion model. The method is based on transforming the edgels of the distorted image to a 1-D angular Hough space, and optimizing the distortion correction parameters which minimize the entropy of the corresponding normalized histogram. Properly corrected imagery will have fewer curved lines, and therefore less spread in Hough space. Since the method does not rely on any image structure beyond the existence of edgels sharing some common orientations and does not use edge fitting, it is applicable to a wide variety of image types. For instance, it can be applied equally well to images of texture with weak but dominant orientations, or images with strong vanishing points. Finally, the method is performed on both synthetic and real data revealing that it is particularly robust to noise.