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2010 International Conference on Machine Vision and Human-machine Interface
Automatic Image Segmentation Using Pulse Coupled Neural Network and Independent Component Analysis
Kaifeng, China
April 24-April 25
ISBN: 978-0-7695-4009-2
| ASCII Text | x | ||
| Cheng Wang, Shaofa Li, Kai He, Zhengchun Lin, Changjin Jiang, "Automatic Image Segmentation Using Pulse Coupled Neural Network and Independent Component Analysis," Machine Vision and Human-machine Interface, International Conference on, pp. 261-263, 2010 International Conference on Machine Vision and Human-machine Interface, 2010. | |||
| BibTex | x | ||
| @article{ 10.1109/MVHI.2010.149, author = {Cheng Wang and Shaofa Li and Kai He and Zhengchun Lin and Changjin Jiang}, title = {Automatic Image Segmentation Using Pulse Coupled Neural Network and Independent Component Analysis}, journal ={Machine Vision and Human-machine Interface, International Conference on}, volume = {0}, year = {2010}, isbn = {978-0-7695-4009-2}, pages = {261-263}, doi = {http://doi.ieeecomputersociety.org/10.1109/MVHI.2010.149}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Machine Vision and Human-machine Interface, International Conference on TI - Automatic Image Segmentation Using Pulse Coupled Neural Network and Independent Component Analysis SN - 978-0-7695-4009-2 SP261 EP263 A1 - Cheng Wang, A1 - Shaofa Li, A1 - Kai He, A1 - Zhengchun Lin, A1 - Changjin Jiang, PY - 2010 KW - Pulse Coupled Neural Network KW - regions of interest KW - Independent Component Analysis KW - image segmentation VL - 0 JA - Machine Vision and Human-machine Interface, International Conference on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MVHI.2010.149
In order to determine the cyclic iteration times of Pulse Coupled Neural Network (PCNN) image segmentation effectively, and obtain the image segmentation result including regions of interest(ROI), an image segmentation method based on PCNN and Independent Component Analysis (ICA) is proposed in this paper. First, extract the independent signal sources corresponding to the image including ROI through ICA. Then, detect the signal sources corresponding to the segmentation result of the each iteration to achieve the output of target image including ROI. The experimental results demonstrate its validity, and the images including ROI correspond to unified independent signal sources. Evaluations of the proposed method are, the average cyclic iteration times N is 5.6, the average runtime is 0.08s, and the accuracy of target image outputs is 98.6%.
Index Terms:
Pulse Coupled Neural Network, regions of interest, Independent Component Analysis, image segmentation
Citation:
Cheng Wang, Shaofa Li, Kai He, Zhengchun Lin, Changjin Jiang, "Automatic Image Segmentation Using Pulse Coupled Neural Network and Independent Component Analysis," mvhi, pp.261-263, 2010 International Conference on Machine Vision and Human-machine Interface, 2010
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