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The detection of curvilinear structures is an important step for variouscomputer vision applications, ranging from medical image analysis forsegmentation of blood vessels, to remote sensing for the identification ofroads and rivers, and to biometrics and robotics, among others. %The visualsystem of the brain has remarkable abilities to detect curvilinear structuresin noisy images. This is a nontrivial task especially for the detection of thinor incomplete curvilinear structures surrounded with noise. We propose ageneral purpose curvilinear structure detector that uses the brain-inspiredtrainable B-COSFIRE filters. It consists of four main steps, namely nonlinearfiltering with B-COSFIRE, thinning with non-maximum suppression, hysteresisthresholding and morphological closing. We demonstrate its effectiveness on adata set of noisy images with cracked pavements, where we achievestate-of-the-art results (F-measure=0.865). The proposed method can be employedin any computer vision methodology that requires the delineation of curvilinearand elongated structures.

Detection of curved lines with B-COSFIRE filters: A case study on crack delineation