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Impact of Intensity Edge Map on Segmentation of Noisy Range Images , Yiyong Sun1

Summary: Impact of Intensity Edge Map on Segmentation of Noisy Range Images
Yan Zhang1
, Yiyong Sun1
, Hamed Sari-Sarraf2
, Mongi A. Abidi1
IRIS Lab, Dept. of ECE, University of Tennessee,
Knoxville, TN 37996-2100, USA
Dept. of EE, Texas Tech University, Lubbock, TX 79409-3102, USA
In this paper, we investigate the impact of intensity edge maps (IEMs) on the segmentation of noisy range images. Two edge-
based segmentation algorithms are considered. The first is a watershed-based segmentation technique and the other is the
scan-line grouping technique. Each of these algorithms is implemented in two different forms. In the first form, an IEM is
fused with the range edge map prior to segmentation. In the second form, the range edge map alone is used. The performance
of each algorithm, with and without the use of the IEM information, is evaluated and reported in terms of correct
segmentation rate. For our experiments, two sets of real range images are used. The first set comprises inherently noisy
images. The other set is composed of images with varying levels of artificial, additive Gaussian noise. The experimental
results indicate that the use of IEMs can significantly improve edge-based segmentation of noisy range images. Considering
these results, it seems that segmentation tasks involving range images captured by noisy scanners would benefit from the use


Source: Abidi, Mongi A. - Department of Electrical and Computer Engineering, University of Tennessee


Collections: Computer Technologies and Information Sciences