Segmentation of stereo terrain images

Debra A. George; Claudio M. Privitera; Theodore T. Blackmon; Eric Zbinden; Lawrence W. Stark

doi:10.1117/12.387204

2 June 2000 Segmentation of stereo terrain images

Debra A. George, Claudio M. Privitera, Theodore T. Blackmon, Eric Zbinden, Lawrence W. Stark

Proceedings Volume 3959, Human Vision and Electronic Imaging V; (2000) https://doi.org/10.1117/12.387204
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

We have studied four approaches to segmentation of images: three automatic ones using image processing algorithms and a fourth approach, human manual segmentation. We were motivated toward helping with an important NASA Mars rover mission task -- replacing laborious manual path planning with automatic navigation of the rover on the Mars terrain. The goal of the automatic segmentations was to identify an obstacle map on the Mars terrain to enable automatic path planning for the rover. The automatic segmentation was first explored with two different segmentation methods: one based on pixel luminance, and the other based on pixel altitude generated through stereo image processing. The third automatic segmentation was achieved by combining these two types of image segmentation. Human manual segmentation of Martian terrain images was used for evaluating the effectiveness of the combined automatic segmentation as well as for determining how different humans segment the same images. Comparisons between two different segmentations, manual or automatic, were measured using a similarity metric, S_AB. Based on this metric, the combined automatic segmentation did fairly well in agreeing with the manual segmentation. This was a demonstration of a positive step towards automatically creating the accurate obstacle maps necessary for automatic path planning and rover navigation.

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Debra A. George, Claudio M. Privitera, Theodore T. Blackmon, Eric Zbinden, and Lawrence W. Stark "Segmentation of stereo terrain images", Proc. SPIE 3959, Human Vision and Electronic Imaging V, (2 June 2000); https://doi.org/10.1117/12.387204

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