Fast color/texture segmentation for outdoor robots
We present a fast integrated approach for online segmentation of images for outdoor robots. A compact color and texture descriptor has been developed to describe local color and texture variations in an image. This descriptor is then used in a two-stage fast clustering framework using K-means to per...
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Published in | 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 4078 - 4085 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.09.2008
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Subjects | |
Online Access | Get full text |
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Summary: | We present a fast integrated approach for online segmentation of images for outdoor robots. A compact color and texture descriptor has been developed to describe local color and texture variations in an image. This descriptor is then used in a two-stage fast clustering framework using K-means to perform online segmentation of natural images. We present results of applying our descriptor for segmenting a synthetic image and compare it against other state-of-the-art descriptors. We also apply our segmentation algorithm to the task of detecting natural paths in outdoor images. The whole system has been demonstrated to work online alongside localization, 3D obstacle detection, and planning. |
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ISBN: | 9781424420575 1424420571 |
ISSN: | 2153-0858 2153-0866 |
DOI: | 10.1109/IROS.2008.4651086 |