Abstract: This paper discusses a system which uses machine vision algorithms for the detection of tubes in an extremely `noisy' industrial environment. The heart of the algorithm consists of sampling of the image in predefined sparse bands perpendicular to the likely orientation of the tubes followed by the application of a normalized correlation as a detection filter over these bands. After assigning a reliability factor to each local maxima of the correlation function, these points are then mapped to the Hough space to determine the equations of the midlines of the tubes. Given these equations, the number of tubes and any positional anomalies are reported. !19
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