|CFP: Neurocomputing Special Issue on Smart Computing for Large Scale Visual Data Sensing and Processing|
We organized a Neurocomputing Special Issue on Smart Computing for Large Scale Visual Data Sensing and Processing:
Papers can be submitted via the online submission system for Neurocomputing (http://ees.elsevier.com/
For more info, please visit:
For more info, please visit:
|Zhihua's paper was accepted by Information Sciences|
Zhihua Chen, Wangmeng Zuo*, Qinghua Hu. Kernel Sparse Representation for Time Series Classification, Information Sciences, 2014 (Accept). (Extention of KSVD and LC-KSVD to kernelized versions and applications to time series classification)
|Shuhang's paper was accepted by NIPS 2014|
Shuhang Gu, Lei Zhang, Wangmeng Zuo, and Xiangchu Feng, “Projective Dictionary Pair Learning for Pattern Classification,” In NIPS 2014. (One of the most efficient method for both training and testing.)
|Faqiang's Paper was accepted by IEEE T-NNLS|
Faqiang Wang, Wangmeng Zuo, Lei Zhang, Deyu Meng, and David Zhang, A Kernel Classification Framework for Metric Learning, IEEE Trans. Neural Networks and Learning Systems, 2014. (Kernel perspective and formulation can provide a number efficient algorithms on metric learning.)
|Zhaoxin's paper was accepted by Image and Vision Computing|
Zhaoxin Li, Kuanquan Wang, Wenyan Jia, Hsin-Chen Chen, Wangmeng Zuo, Deyu Meng , Mingui Sun, Multiview Stereo and Silhouette Fusion via Minimizing Generalized Reprojection Error, Image and Vision Computing, 2014 (Our first paper on multiview stero. We proposed a two-phase optimization method for generalized reprojection error minimization. In terms of accuracy and completeness, our TwGREM achieves high rank on the Middlebury evaluation page for dino sparse and temple sparse.)
|Peng's paper was accepted by IEEE JBHI|
Wangmeng Zuo, Peng Wang, David Zhang. Comparison of three different types of wrist pulse signals by their physical meanings and diagnosis performance, IEEE Journal of Biomedical and Health Informatics, 2014 (Accept). (A comprehensive analysis and comparative study on three major types of wrist pulse signals.)
|CFP: Pattern Recognition Special Issue on Compositional Models and Structured Learning for Visual Recognition|
In the last ten years, computer vision and pattern recognition has experienced a resurgence of research on compositional and hierarchical models, such as And-Or graphs, deformable part-based models, kernelized and latent variable models. The virtue of compositional and hierarchical models (CHMs) lies in their expressive power to model diverse and complex visual patterns. Meanwhile, a set of structured learning and optimization methods are intensively discussed to facilitate training and inference with compositional models, which usually integrate latent structures to specify the task-specific compositional configurations and contextual relations. These methods, such as latent support vector machines, conditional random fields, and structural sparse coding, enable inference with rich internal structures and pursue a good mapping between observations and output structured predictions. Compared with the neural networks, which have also attracted much attention recently, CHMs and structured learning methods provide alternative approaches to explicitly handle the variations of data with latent variables, and demonstrate their potential in several high-level vision tasks, e.g., object detection and recognition, scene parsing, and action/activity understanding.
In order to pursue first-class research along this direction, we would like to organize a special issue titled "Compositional Model and Structured Learning for Visual Recognition" in the journal of Pattern Recognition.
The issue will be aimed at accepting papers on the following topics but not limited to:
The main timelines for this issue are set as follows,
All submissions for this special issue are required to follow the same format as regular full-length Pattern Recognition papers. The submission website for this special issue is located at: http://ees.elsevier.com/pr/. Please ensure to select 'SI : CHM-Vision' as the 'Article Type'.
Professor Liang Lin
Associate Professor Jason Corso
Associate Professor Wangmeng Zuo
Chair Professor David Zhang
Dr Benjamin Yao
|Two papers were accepted by CVPR 2015.|
 Wangmeng Zuo, Dongwei Ren, Shuhang Gu, Liang Lin, Lei Zhang, Discriminative Learning of Iteration-wise Priors for Blind Deconvolution, CVPR 2015.
 Chenglong Li, Liang Lin, Wangmeng Zuo, Jin Tang, Shuicheng Yan, SOLD: Sub-Optimal Low-Rank Decomposition for Efficient Video Segmentation, CVPR 2015.
|Two papers were accepted by ICCV 2015|
272: Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising
1383: Convolutional Sparse Coding for Image Super-resolution
Congratulations to Shuhang and Jun!
|Kai's super-resolution paper was accepted by IEEE SPL|
Kai Zhang, Baoquan Wang, Wangmeng Zuo*, Hongzhi Zhang, Lei Zhang, Joint Learning of Multiple Regressors for Single Image Super-Resolution, IEEE Signal Processing Letters, 2015 (An efficient and effective approach for single image super-resolution by joint learning of partitions and regressors ).
Congratulation to Kai!
|Zhaoxin's MVS paper was accepted by IEEE TIP|
Zhaoxin Li, Kuanquan Wang, Wangmeng Zuo, Deyu Meng, Lei Zhang. Detail-preserving and Content-aware Variational multi-view stereo reconstruction. IEEE Trans. Image Processing, 2016 (best results among all published methods on the Middlebury dino ring and dino sparse datasets).
|2007.8 - 2010.10||讲师|