Abstract
This paper describes the method to know objects for autonomous robot navigation in an unknown outdoor environment. The method segments the objects from an image taken by moving robot on outdoor environment. In the beginning object segmentation, this uses multiple features to obtain the objects of segmented region. Multiple features are color, context information, line segments, edge, Hue Co-occurrence Matrix (HCM), Principal Components (PCs) and Vanishing Points (VPs). The model of the objects for outdoor environment defines their characteristics individually. We segment the region as mixture using the proposed features and methods. Next the stage classifies the object into natural and artificial ones. We detect sky and trees of natural object and detect building of artificial object using the combination of appearance and context information. Then we estimate the dimensions of building. Extensive experiments with the object segmentation and analysis on outdoor environment confirm the validity of the approach.
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© 2009 Springer-Verlag Berlin Heidelberg
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Kim, DN., Trinh, HH., Jo, KH. (2009). Object Analysis for Outdoor Environment Perception Using Multiple Features. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_66
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DOI: https://doi.org/10.1007/978-3-642-04070-2_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04069-6
Online ISBN: 978-3-642-04070-2
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