Impact of intensity edge map on segmentation of noisy range images

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 evalute 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 compared 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 result, it seems that segmentation tasks invovling 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.