High Accuracy Feature Detection for Camera Calibration: A Multi-steerable Approach

Abstract

We describe a technique to detect and localize features on checkerboard calibration charts with high accuracy. Our approach is based on a model representing the sought features by a multiplicative combination of two edge functions, which, to allow for perspective distortions, can be arbitrarily oriented.

First, candidate regions are identified by an eigenvalue analysis of the structure tensor. Within these regions, the sought checkerboard features are then detected by matched filtering. To efficiently account for the double-oriented nature of the sought features, we develop an extended version of steerable filters, viz., multi-steerable filters. The design of our filters is carried out by a Fourier series approximation. Multi-steerable filtering provides both the unknown orientations and the positions of the checkerboard features, the latter with pixel accuracy. In the last step, the feature positions are refined to subpixel accuracy by fitting a paraboloid. Rigorous comparisons show that our approach outperforms existing feature localization algorithms by a factor of about three.