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Curvilinear object detection is the common denominator of several applications. Some illustrative examples are road detection from aerial or satellite images, human airways from volumetric 3D scans or vascular structures in eye-fundus images. In this work, we propose two general-purpose curvilinear object detectors that may serve as building blocks for application-specific systems. To do so, we employ fuzzy mathematical morphology operators due to their robustness with respect to uncertainty and noise, and the trade-off they offer between expressive power and computational requirements. The extraction of linear features is based on, respectively, the fuzzy hit-or-miss transform and the fuzzy top-hat transform. They can be customized depending on the width of the objects of interest. We compare these two approaches with other state-of-the-art, general-purpose curvilinear object detectors to highlight their …