Road Image Segmentation and Recognition Using Hierarchical Bag-of-Textons Method View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2011

AUTHORS

Yousun Kang , Koichiro Yamaguchi , Takashi Naito , Yoshiki Ninomiya

ABSTRACT

While the bag-of-words models are popular and powerful method for generic object recognition, they discard the context information for spatial layout. This paper presents a novel method for road image segmentation and recognition using a hierarchical bag-of-textons method. The histograms of extracted textons are concatenated to regions of interest with multi-scale regular grid windows. This method can learn automatically spatial layout and relative positions between objects in a road image. Experimental results show that the proposed hierarchical bag-of-textons method can effectively classify not only the texture-based objects, e.g. road, sky, sidewalk, building, but also shape-based objects, e.g. car, lane, of a road image comparing the conventional bag-of-textons methods for object recognition. In the future, the proposed system can combine with a road scene understanding system for vehicle environment perception. More... »

PAGES

248-256

References to SciGraph publications

Book

TITLE

Advances in Image and Video Technology

ISBN

978-3-642-25366-9
978-3-642-25367-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-25367-6_22

DOI

http://dx.doi.org/10.1007/978-3-642-25367-6_22

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025836088


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