Impact of intensity edge map on segmentation of noisy range images

Yan Zhang, Yiyong Sun, Hamed Sari-Sarraf, Mongi A. Abidi

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations


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 of IEM information. Additionally, the experiments indicate that higher quality edge information can be obtained by fusing range and intensity edge information.

Original languageEnglish
Pages (from-to)260-269
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2000
EventThree-Dimensional Image Capture and Applications III - San Jose, CA, USA
Duration: Jan 24 2000Jan 25 2000


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