Bea2006a

[Bea2006a]
Pseudo-Convex Contour Criterion for Hierarchical Segmentation of SAR Images

Author:Beaulieu Jean-Marie

Conference:The 3rd Canadian Conference on Computer and Robot Vision

 Laval University, Canada

 June 07-09, 2006, pp. 29-29

ISBN:0-7695-2542-3

URL:http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1640384

DOI:10.1109/CRV.2006.58

Abstract:   The hierarchical segmentation of SAR (Synthetic Aperture Radar) images is greatly complicated by the presence of coherent speckle. We are exploring the utilization of spatial constraints and contour shapes in order to improve the segmentation results. With standard merging criterion, the high noise level of SAR images results in the production of regions that have variable mean and variance values and irregular shapes. If the first segments are not correctly delimited then the following steps will merge segments from different fields. In examining the evolution of the initial segments, we see that the merging should take into account spatial aspects. Particularly, the segment contours should have good shapes. In this paper, we examine how the pseudo-convex envelope of a region can be used to evaluate the region contour. We present a pseudo-convex measure adapted to the geometry of image lattice. We show how the pseudo-convex envelope can be calculated. We present measures comparing contour shapes and using the perimeter, the area and the boundary length of segments. We use a hierarchical segmentation algorithm based upon stepwise optimization. A stepwise merging criterion is derived from the multiplicative speckle noise model. The shape measures are combined with the merging criterion in order to guide correctly the segment merging process. The new criterion produces good segmentation of SAR images. This is illustrated by synthetic and real image results.

Pseudo-Convex Contour Criterion for Hierarchical Segmentation of SAR Images,
Beaulieu Jean-Marie,
The 3rd Canadian Conference on Computer and Robot Vision, Laval University, Canada, June 07-09, 2006, pp. 29-29.
[Bibtex]

@Conference{Bea2006a,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Pseudo-Convex Contour Criterion for Hierarchical Segmentation of {SAR} Images},
booktitle = {The 3rd Canadian Conference on Computer and Robot Vision},
volume = {},
publisher = {IEEE},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1640384},
isbn = {0-7695-2542-3},
doi = {10.1109/CRV.2006.58},
address = {Laval University, Canada},
pages = {29-29},
year = {2006},
month = {June 07-09,},
abstract = {The hierarchical segmentation of SAR (Synthetic Aperture Radar) images is greatly complicated by the presence of coherent speckle. We are exploring the utilization of spatial constraints and contour shapes in order to improve the segmentation results. With standard merging criterion, the high noise level of SAR images results in the production of regions that have variable mean and variance values and irregular shapes. If the first segments are not correctly delimited then the following steps will merge segments from different fields. In examining the evolution of the initial segments, we see that the merging should take into account spatial aspects. Particularly, the segment contours should have good shapes. In this paper, we examine how the pseudo-convex envelope of a region can be used to evaluate the region contour. We present a pseudo-convex measure adapted to the geometry of image lattice. We show how the pseudo-convex envelope can be calculated. We present measures comparing contour shapes and using the perimeter, the area and the boundary length of segments. We use a hierarchical segmentation algorithm based upon stepwise optimization. A stepwise merging criterion is derived from the multiplicative speckle noise model. The shape measures are combined with the merging criterion in order to guide correctly the segment merging process. The new criterion produces good segmentation of SAR images. This is illustrated by synthetic and real image results.},
mypdf = {11},
keywords = {Area measurement; Geometry; Image segmentation; Lattices; Merging; Noise level; Production; Shape measurement; Speckle; Synthetic aperture radar},
openpdf = {},
openid = {}
}

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Published in: The 3rd Canadian Conference on Computer and Robot Vision (CRV’06)
Date of Conference: 07-09 June 2006
Publisher: IEEE