A Perturbation Suppressing Segmentation Technique Based on Adaptive Diffusion

Authors:
Wolfgang Middelmann;Alfons Ebert;Tobias Deißler;Ulrich Thoennessen
Affiliations:
FGAN-FOM Research Institute for Optronics and Pattern Recognition, Germany;FGAN-FOM Research Institute for Optronics and Pattern Recognition, Germany;FGAN-FOM Research Institute for Optronics and Pattern Recognition, Germany;FGAN-FOM Research Institute for Optronics and Pattern Recognition, Germany
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Year:
2007

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Abstract

Segmentation is a fundamental task in pattern recognition and basis for high level applications like scene reconstruction, change detection, or object classification. The performance of these tasks suffers from a distorted segmentation. In this contribution an adaptive diffusion-based segmentation method is proposed suppressing perturbations in the segmentation with focusing on small regions with high contrast to their surrounding. The algorithm determines in each step the diffusion tensor. It is re-weighted with respect to an assessment stage. A comparative study uses high-resolution remote sensing data.