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Proceedings Paper

Learning to detect objects in natural image using Texton cues
Author(s): Taisong Jin; Lingling Li; Cuihua Li
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Paper Abstract

In this paper, an object extraction algorithm from complex scenes is presented. Firstly, Textons are modeled by the joint distribution of filter responses. This distribution is represented by Texton (cluster centre) frequencies. Secondly, classification of a novel image proceeds by mapping the image to a Texton distribution and comparing this distribution to the learnt models. So the detection of possible object regions is performed. During the verification stage, the knowledge about the scene and the geometry of the objects is represented by means of t graph, and especially, the knowledge about the surrounding of the object is used in order to support the detection of individual objects. Finally, Bayes nets are selected to verify those possible objects as a useful tool. The test on the dataset in building scenes shows that the proposed algorithm has a better performance, compared with the similar methods.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74962B (30 October 2009); doi: 10.1117/12.832969
Show Author Affiliations
Taisong Jin, Xiamen Univ. (China)
Lingling Li, Zhengzhou Institute of Aeronautical Industry Management (China)
Cuihua Li, Xiamen Univ. (China)

Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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