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 percent success rate on 1,737 images), of the fovea in the retina (99.32 percent success rate on 1,616 images), and of the pupil in regular camera images (95.86 percent on 1,521 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.

中文翻译：

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旋转翻译组上通过密度进行模板匹配

我们提出了一种模板匹配方法，用于检测以方向图为特征的2D图像对象。我们的方法基于通过方位分数的数据表示，方位分数是位置和方位空间的函数，并且是通过小波类型变换获得的。这种新的表示方式使我们能够以直观，直接的方式（即通过互相关）检测方向图。此外，我们提出了一个通用的线性回归框架，用于使用平滑样条线构建合适的模板。在这里，重要的是要在位置定向域上识别弯曲的几何形状，这是我们用李群SE（2）识别的：旋转平移群。然后以B样条为基础对模板进行优化，并针对弯曲的几何体定义平滑度。我们在三种不同的应用上获得了最新的结果：检测视网膜中的视神经乳头（在1,737幅图像上的成功率为99.83％），视网膜中央凹（在1,616幅图像上的成功率为99.32％），和普通相机图像中的瞳孔（在1,521张图像中占95.86％）。高性能是由于在模板匹配中同时包含强度和方向特征以及有效的几何先验。此外，由于基于互相关的匹配方法，我们的方法速度很快。高性能是由于在模板匹配中同时包含强度和方向特征以及有效的几何先验。此外，由于基于互相关的匹配方法，我们的方法速度很快。高性能是由于在模板匹配中同时包含强度和方向特征以及有效的几何先验。此外，由于基于互相关的匹配方法，我们的方法速度很快。