Coverage segmentation based on linear unmixing and minimization of perimeter and boundary thickness

Lindblad, JoakimSwedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen för visuell information och interaktion,,Centre for Image Analysis (författare)

We present a method for coverage segmentation, where the, possibly partial, coverage of each image element by each of the image components is estimated. The method combines intensity information with spatial smoothness criteria. A model for linear unmixing of image intensities is enhanced by introducing two additional conditions: (i) minimization of object perimeter, leading to smooth object boundaries, and (ii) minimization of the thickness of the fuzzy object boundary, and to some extent overall image fuzziness, to respond to a natural assumption that imaged objects are crisp, and that fuzziness is mainly due to the imaging and digitization process. The segmentation is formulated as an optimization problem and solved by the Spectral Projected Gradient method. This fast, deterministic optimization method enables practical applicability of the proposed segmentation method. Evaluation on both synthetic and real images confirms very good performance of the algorithm.

Sladoje, Natasa (författare)
Uppsala universitetBildanalys och människa-datorinteraktion (creator_code:org_t)
Sveriges lantbruksuniversitet

Ingår i:Pattern Recognition Letters: Elsevier BV33:6, s. 728-7380167-86551872-7344

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