Impact of Intensity Edge Map on Segmentation of Noisy Range Images

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Impact of Intensity Edge Map on Segmentation of Noisy Range Images (2000)

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

Venue:	Proceedings of SPIE Conference on ThreeDimensional Image Capture and Application III
Citations:	8 - 3 self

BibTeX

@INPROCEEDINGS{Zhang00impactof,
    author = {Yan Zhang and Yiyong Sun and Hamed Sari-sarraf and Mongi A. Abidi},
    title = {Impact of Intensity Edge Map on Segmentation of Noisy Range Images},
    booktitle = {Proceedings of SPIE Conference on ThreeDimensional Image Capture and Application III},
    year = {2000},
    pages = {260--269}
}

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Abstract

In this paper, we investigate the impact of intensity edge maps (IEMs) on the segmentation of noisy range images. Two edgebased 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. Keywords: range image segmentation, edge detection, edge fusion, watershed 1.

Keyphrases

noisy range image intensity edge map iem information range edge map range image segmentation scan-line grouping technique edge detection noisy scanner range image first set comprises additive gaussian noise second form segmentation task first form real range image noisy image correct segmentation rate different form intensity edge information watershed-based segmentation technique edge-based segmentation experimental result edgebased segmentation algorithm edge fusion

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