The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. %The visual system of the brain has remarkable abilities to detect curvilinear structures in noisy images. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morphological closing. We demonstrate its effectiveness on a data set of noisy images with cracked pavements, where we achieve state-of-the-art results (F-measure=0.865). The proposed method can be employed in any computer vision methodology that requires the delineation of curvilinear and elongated structures.

我们提出了一种使用基于大脑灵感的可训练B-COSFIRE滤波器的通用曲线结构检测器，可用于任何需要描绘曲线和细长结构的计算机视觉方法。通过在含有破裂路面的噪声图像数据集上达到最优效果，我们证明了这种方法的有效性（F-measure = 0.865）。

基于B-COSFIRE滤波器的曲线检测：以裂纹勾画为例