Abstract: A common challenge in many image analysis applications is the segmentation of the data into objects of interest (foreground) and background. Typically, the contrast of the data and the background greylevel varies across the image, thereby requiring some form of normalization prior to a threshold step. Previous morphological techniques for data normalization have been quite successful. These approaches include the `rolling ball' of Sternberg and the `top hat' of Serra. These techniques are particularly effective for patterns of interest that are spaced relatively far apart compared to their width. In contrast, the algorithm introduced in this paper is applicable to closely spaced thin broken lines on a highly variable background. Using a combination of greyscale morphology and conditional masking of image data, the approach is shown to be highly robust for closely spaced foreground/background patterns.!13
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