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The automatic extraction of elongated curvilinear structures (CLSs) is an important task in various image processing applications, including numerous remote sensing, and biometrical and medical problems. To address this task, we develop a stochastic approach that relies on a fixed-grid, localized Radon transform for line segment extraction and a conditional random field model to incorporate local interactions and refine the extracted CLSs. We propose several different energy data terms, the appropriate choice of which allows us to process images with different noise and geometry properties. The contribution of this paper is the design of a flexible and robust elongated CLS extraction framework that is comparatively fast due to the use of a fixed-grid configuration and fast deterministic Radon-based line detector. We present several different applications of the developed approach, namely: 1) CLS extraction in …