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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Bibliographic Details
Main Authors: Blas, M.R., Agrawal, M., Sundaresan, A., Konolige, K.
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Filter bank
Histograms
Image color analysis
Image segmentation
Pixel
Robots
Online Access:Request full text
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Description
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.
ISSN:2153-0858
2153-0866
DOI:10.1109/IROS.2008.4651086