Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#264912] of Guillermo Sapiro

Papers Published

  1. Haker, S; Sapiro, G; Tannenbaum, A, Knowledge-based segmentation of SAR images, Ieee International Conference on Image Processing, vol. 1 (December, 1998), pp. 597-601, IEEE Comput. Soc [doi]
    (last updated on 2019/06/26)

    A new approach for the segmentation of still and video SAR images is described in this paper. A priori knowledge about the objects present in the image, e.g., target, shadow, and background terrain, is introduced via Bayes' rule. Posterior probabilities obtained in this way are then anisotropically smoothed, and the image segmentation is obtained via MAP classifications of the smoothed data. When segmenting sequences of images, the smoothed posterior probabilities of past frames are used to learn the prior distributions in the succeeding frame. We show, via a large number of examples from public data sets, that this method provides an efficient and fast technique for addressing the segmentation of SAR data.
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320