Abstract
Segmentation of digital images is a key problem for automation of object recognition and object reconstruction. Area based segmentations, like region growing and split-and-merge, may use similarity with respect to some grey level property as predicate for appending pixels to a region. Regions of particular interest, for example roofs of buildings in aerial photographs, are seldom homogeneous with respect to grey level. There are often position dependent trends in the grey values of a region, that is; the image is piecewise smooth. These trends may be modelled by functional fitting. This turns image segmentation into a computer intensive least squares problem. In this article, a split-and-merge procedure using array algebra is proposed to make polynomial fitting faster. The splitting is two to three times faster than using regular matrix algebra. For 3D images it is four to nine times faster. The merging is performed without accessing the image.
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This study was financed by the Swedish Council for Building Research (BFR).
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Wiman, H. Array algebra polynomial fitting for image segmentation. J Math Imaging Vis 6, 7–13 (1996). https://doi.org/10.1007/BF00127372
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DOI: https://doi.org/10.1007/BF00127372