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Thousands of miles of railroad track must be inspected twice weekly by a human inspector to maintain safety standards. A computer vision system, consisting of field-acquired video and subsequent analysis, could improve the efficiency of the current methods. Such a system is prototyped, and the following challenges are addressed: the detection, segmentation, and defect assessment of track components whose appearance vary across different tracks and the identification and inspection of special track areas such as track turnouts. An algorithm that utilizes the periodic manner in which track components repeat in an inspection video is developed. Spectral estimation and signal-processing methods are used to provide robust detection of the periodically occurring track components. Results are demonstrated on field-acquired images and video.