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Proceedings Paper

Watersheds-based segmentation integrated with edge detection
Author(s): Huang Yao; Jianya Gong; Wanshou Jiang
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Paper Abstract

Image segmentation refers to the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analysis [1]. These advantages of this process make it be widely used to find out the interesting objects in high resolution remote sensing images. Watershed algorithm is based on the topology of the image, although it can easily split images into homogenous partitions, it also leads to over-segment in practical implementation. Some solutions were proposed to solve this problem in the past few years [2][3]. Using pre-defined seeds and extracting the pixels clusters which are grown from these seeds is a reasonable method to overwhelm the obscure of over-segmentation. In this paper; we present a novel framework to improve the results of watersheds segmentation by using edge detection to find out the position of seed points. Then the immersion simulations suggested by Vicent has been used to segment the image. The algorithm consists of four steps: a) Edge detection with embedded confidence, b) Thinning processing on the previous results, c) Label the seed points on each side of the thinned edges, d) Detection of watersheds on gradient magnitude image using immersion simulations. At last, we use high resolution remote sensing image to qualify the framework and the experimental results are presented. It shows that reasonable results which preserve the edge could be gotten by applying this framework.

Paper Details

Date Published: 13 October 2009
PDF: 9 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 749208 (13 October 2009); doi: 10.1117/12.838369
Show Author Affiliations
Huang Yao, Wuhan Univ. (China)
Jianya Gong, Wuhan Univ. (China)
Wanshou Jiang, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining
Yaolin Liu; Xinming Tang, Editor(s)

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