In studying image management in a side effect manner, we got involved in track detection. In broader terms the problem we considered is that of image-processing/feature-extraction. Such a problem has applications in the area of automated target detection and tracking; an area quite significant and evident in systems such as radar, sonar, radioasronomy, etc. This work demonstrates how the problem of tracking targets, which appear as either straight or curved lines in two dimensional display images (or data images) can be formulated in terms of a directed weighted graph model and how dynamic programming techniques can be efficiently applied to reach an optimal or sub-optimal solution. In general, track detection algorithms providing optimal solutions have good detective ability, but most of them suffer from the inability to detect discontinuous lines or to resolve efficiently pairs of crossing lines. A sub-optimal solution is provided that efficiently overcome these weaknesses. We focus on modeling the track detection problem in terms of a graph, formulating fast sequential/parallel sub-optimal track detection algorithms and testing them on simulated data in order to show their detective ability. Moreover, we specify the conditions under which sub-optimal algorithms can perform at least as well as their corresponding optimal algorithms. This is significant for the track detection problem where fast, accurate and real-time detection is considered a necessity.

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