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30 October 2009 Learning to detect objects in natural image using Texton cues
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Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74962B (2009)
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Taisong Jin, Lingling Li, and Cuihua Li "Learning to detect objects in natural image using Texton cues", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74962B (30 October 2009);

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