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New method for unsupervised segmentation of moving objects in infrared videos

[+] Author Affiliations
Chaobo Min

Nanjing University of Science and Technology, School of Electronic Engineering and Optic-electronic Technology, Nanjing, Jiangsu Province, 210094 China

Junju Zhang

Nanjing University of Science and Technology, School of Electronic Engineering and Optic-electronic Technology, Nanjing, Jiangsu Province, 210094 China

Benkang Chang

Nanjing University of Science and Technology, School of Electronic Engineering and Optic-electronic Technology, Nanjing, Jiangsu Province, 210094 China

Baohui Zhang

Nanjing University of Science and Technology, School of Electronic Engineering and Optic-electronic Technology, Nanjing, Jiangsu Province, 210094 China

Yingjie Li

Nanjing University of Science and Technology, School of Electronic Engineering and Optic-electronic Technology, Nanjing, Jiangsu Province, 210094 China

J. Electron. Imaging. 22(4), 043026 (Dec 16, 2013). doi:10.1117/1.JEI.22.4.043026
History: Received March 19, 2013; Revised September 16, 2013; Accepted October 7, 2013
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Abstract.  A new method for unsupervised segmentation of moving objects in infrared videos is presented. This method consists of two steps: difference image quantization and spatial segmentation. In the first step, the changed pixels in the difference image are quantized to several classes by using Bayes decision. It can be used to cluster the changed pixels belonging to the same moving object together. The pixels of the difference image are replaced by their corresponding class labels, thus forming a class-map of the difference image. In the second step, each class in the class-map is considered as a subset of the possible seeds of moving objects. A self-adaptive region growing method is then used to image segmentation on the basis of these different subsets. One of the focuses of this work is on spatial segmentation, where a criterion is proposed for evaluation of moving object segmentation without ground truth in infrared videos. This criterion is used to evaluate the performance of the segmentation masks grown from different subsets of the possible seeds. The best segmented image is determined to be the final segmentation result. Experiments show the advantage and robustness of the proposed algorithm on real infrared videos.

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© 2013 SPIE and IS&T

Topics

Quantization ; Video

Citation

Chaobo Min ; Junju Zhang ; Benkang Chang ; Baohui Zhang and Yingjie Li
"New method for unsupervised segmentation of moving objects in infrared videos", J. Electron. Imaging. 22(4), 043026 (Dec 16, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.4.043026


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