This special issue of International Journal of Computer Mathematics outlines a selection of manuscripts presented at the third Sino-foreign-interchange Workshop on Intelligent Science and Intelligent Data Engineering held in 2012 in Nanjing, which promotes the academic exchange of research in intelligent science and intelligent data engineering in China and abroad. This Workshop has attracted over 300 submissions.
The papers presented in this issue, which were guest edited by Professor Fang Fang of Peking University and Professor Xinbo Gao of Xidian University, underwent post-conference extensions and only contain novel research. These papers also underwent the usual rigorous peer-review which is a cornerstone of IJCM. The result is an interesting small selection of papers that highlight the research presented at this conference, and which advance the fields of intelligent science and intelligent data engineering, particularly within: pattern recognition; computer vision and image processing; machine learning and computational intelligence; knowledge discovery and data mining; graphics and computer visualization; and multimedia processing and application.
The papers comprising the special issue are listed below:
An approach to facial expression analysis with multi-model interactions by Chao Xu, Qinghua Hu, Guangquan Xu & Zhiyong Feng [Citation9].
Optimal representation set construction with a competitive scheme for palmprint and face recognition by Jia-Jun Wen, Jin-Rong Cui, Zhi-Hui Lai, Jian-Xun Mi & Xiao-Tang Guo [Citation8].
Extraction of lane markings using orientation and vanishing point constraints in structured road scenes by Weirong Liu, Shutao Li & Xu Huang [Citation6].
Objective image quality assessment: a survey by Lihuo He, Fei Gao, Weilong Hou & Lei Hao [Citation5].
Semi-supervised learning with local and global consistency by Jie Gui, Rongxiang Hu, Zhongqiu Zhao & Wei Jia [Citation3].
Pseudo-Zernike moment invariants to blur degradation and similarity transformation by Xiubin Dai, Tianliang Liu, Huazhong Shu & Limin Luo [Citation1].
Dayside aurora classification via BIFs-based sparse representation using manifold learning by Bing Han, Xiaojing Zhao, Dacheng Tao, Xuelong Li, Zejun Hu & Hongqiao Hu [Citation4].
Synthetic aperture radar image segmentation based on well-initialized active contours by Weiping Ni, Jianlong Zhang & Weidong Yan [Citation7].
An additive convolution model for fast restoration of nonuniform blurred images by Hong Deng, Wangmeng Zuo, Hongzhi Zhang & David Zhang [Citation2].
On behalf of the Guest Editors, I hope you enjoy reading this special issue.
REFERENCES
- X. Dai, T. Liu, H. Shu, and L. Luo, Pseudo-Zernike moment invariants to blur degradation and similarity transformation, Int. J. Comput. Math. 91(11) (2014), pp. 2403–2414.
- H. Deng, W. Zuo, H. Zhang, and D. Zhang, An additive convolution model for fast restoration of nonuniform blurred images, Int. J. Comput. Math. 91(11) (2014), pp. 2446–2466.
- J. Gui, R. Hu, Z. Zhao, and W. Jia, Semi-supervised learning with local and global consistency, Int. J. Comput. Math. 91(11) (2014), pp. 2389–2402.
- B. Han, X. Zhao, D. Tao, X. Li, Z. Hu, and H. Hu, Dayside aurora classification via BIFs-based sparse representation using manifold learning, Int. J. Comput. Math. 91(11) (2014), pp. 2415–2426.
- L. He, F. Gao, W. Hou, and L. Hao, Objective image quality assessment: A survey, Int. J. Comput. Math. 91(11) (2014), pp. 2374–2388.
- W. Liu, S. Li, and X. Huang, Extraction of lane markings using orientation and vanishing point constraints in structured road scenes, Int. J. Comput. Math. 91(11) (2014), pp. 2359–2373.
- W. Ni, J. Zhang, and W. Yan, Synthetic aperture radar image segmentation based on well-initialized active contours, Int. J. Comput. Math. 91(11) (2014), pp. 2427–2445.
- J.-J. Wen, J.-R. Cui, Z.-H. Lai, J.-X. Mi, and X.-T. Guo, Optimal representation set construction with a competitive scheme for palmprint and face recognition, Int. J. Comput. Math. 91(11) (2014), pp. 2341–2358.
- C. Xu, Q. Hu, G. Xu, and Z. Feng, An approach to facial expression analysis with multi-model interactions, Int. J. Comput. Math. 91(11) (2014), pp. 2329–2340.