We propose a template matching method for the detection of 2D image objects
that are characterized by orientation patterns. Our method is based on data
representations via orientation scores, which are functions on the space of
positions and orientations, and which are obtained via a wavelet-type
transform. This new representation allows us to detect orientation patterns in
an intuitive and direct way, namely via cross-correlations. Additionally, we
propose a generalized linear regression framework for the construction of
suitable templates using smoothing splines. Here, it is important to recognize
a curved geometry on the position-orientation domain, which we identify with
the Lie group SE(2): the roto-translation group. Templates are then optimized
in a B-spline basis, and smoothness is defined with respect to the curved
geometry. We achieve state-of-the-art results on three different applications:
detection of the optic nerve head in the retina (99.83% success rate on 1737
images), of the fovea in the retina (99.32% success rate on 1616 images), and
of the pupil in regular camera images (95.86% on 1521 images). The high
performance is due to inclusion of both intensity and orientation features with
effective geometric priors in the template matching. Moreover, our method is
fast due to a cross-correlation based matching approach.

本文提出了一种基于方向图案匹配的二维图像对象检测方法，使用方向得分表示数据，通过交叉相关性实现图案的直观和直接检测，并通过使用平滑样条构建适当的模板和优化 B 样条基础来发现位置方向上的曲面几何，取得了在三种不同的应用程序中最先进的结果：视网膜中的视神经头检测（1737 张图像上的 99.83％成功率），视网膜中的黄斑检测（1616 张图像上的 99.32％成功率）以及常规照相机图像中的瞳孔检测（1521 张图像上的 95.86％）。