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Morphological algorithm development case study: detection of shapes in low-contrast gray-scale images with replacement and clutter noise

This paper presents a case study of the design of a fully autonomous morphological detection algorithm. Grayscale input images contain objects to be detected among difficult clutter, replacement noise, and background tilt. The criteria for choosing algorithm structure is included, with associated grayscale and binary structuring elements based upon comparing the geometry of target and noise/clutter objects. Background cancellation is discussed, along with histogram-based techniques for final thresholding to binary detection images. Finally a performance characterization methodology for the detection algorithm is presented. In addition to conventional detection statistics, the authors consider the 'quality' of the hits and false alarms, vis-a-vis the feature set and classifier used in classification downstream of the detector in the overall system design.