Welcome to MC Lab. EI Dept. HUST

      Media and Communication Laboratory is one branch of Wireless Broadband Communication and Multimedia System Research Center，Department of Electronics and Information Engineering, Huazhong University of Science and Technology. Our research fields include rate-distortion theory for wireless channel, multimedia information processing, media content and security, wireless sensor network, vision computing and computer graphics.
      This lab has close cooperation with several international universities, including University of California at Los Angeles, Temple University,U.S. and McGill University, Canada.
      In 2009, MC Lab. got a great progress in research fields. Some papers have been published on top journal including IEEE Trans on PAMI, IEEE Trans on IP, and top conferences, INFOCOM, CVPR. More detail see "Select Publications in MC Lab".

 Visual Computing Group

        Visual Computing Group is a research group under Media and Communication Lab. Our research fields are shape analysis and data mining. Current research interests include shape analysis, object recognition, geometric modeling, and 3D model retrieval. Our group has established cooperative relations with many other research groups, such as Dept. of Neurology, University of California, Los Angeles, CIS Dept. of Temple University, Microsoft Research Asia and Lotus Hill Institute.

Shape Analysis:

1) Shape Representation

        Skeleton, or called Medial Axis, is a very popular shape descriptor for shape representation and recognition. The drawback of skeleton is its sensitivity to boundary noises and deformations: little noise or a variation of the boundary often generates redundant skeleton branches that may seriously disturb the topology of the skeleton’s graph. As shown in Fig (a), the skeleton of the horse is too grassy because of the noises and the short corners on the contour. Thus, skeleton pruning is usually an essential way to obtain the visual skeletons required by skeleton-based shape matching. Fig (b) shows the visual skeleton of a horse, which is the pruned skeleton of (a) with the proposed pruning method.

(a)                              (b)

2) Shape Matching and Classification

        Skeleton-based shape matching often has two difficulties: 1) Some skeletons are too grassy to be matched; 2) Graph matching is an NPC problem, so the complexity of the skeleton matching algorithm is usually too high. At the beginning, all the skeletons have been pruned with our pruning method. The main idea is to match skeleton graphs by comparing the geodesic paths between skeleton endpoints. In this way, skeleton graph can be transferred into vector sequence. Thus, the step for skeleton matching can be transferred into the step for sequence matching.

3) Visual Curvature

        Visual curvature is a new definition of curvature; it can apply to regular curves as defined in differential geometry as well as to turn angles of polygonal curves. In this way, we unify the definition of curvature on both regular curves and polygonal curves.
        Visual curvature is multi-scale; it can ignore small details when calculating the curvature at a relative larger scale. Thus, it is suitable for many visual processing, such as curve evolution and salient point detection.

Curve Evolution of a noisy pentagra

Detected Corners of a noisy plane

4) Data Mining & Machine Learning
        There exist many data mining techniques recently. Our team aims at implementing these techniques as tools in the computer vision, which help us find the useful and potential information for low, middle and high level vision problems such as segmentation and object recognition. Another research topic focuses on optimization of the classification and clustering models. One interesting and challenging work of our team is committed to find the optimal model selection scheme for these models and analyze the influence of the unbalance training data.

Some documents in related domain must be read and some skills are required in our group.

Related Domain:

Computer Graphics, Computer Vision, Pattern Recognition, Digital Image Processing, Machine Learning, Data Mining, Geometry Modeling and etc.

Skill required:

Mathematics: Basic Statistics Theory, Probability and Random Process, Functional Analysis, Manifold, Matrix Theory, Differential Geometry, Integral Geometry.

Programming: Qualified programming skill on C and C++, basic programming skill for 3D models (OpenGL, or Direct3D), familiar with Matlab and Latex. Java or C# is also encouraged.

English: Excellent reading and writing, good speaking English (recommended).

Publications:

[1].Quannan Li, Yu Zheng, Xing Xie, Yukun Chen, Wenyu Liu, Wei-Ying Ma, "Mining User Similarity Based on Location History", ACM GIS 2008.PDFNEW

[2].Xiang Bai, Xingwei Yang, L.J. Latecki, and Wenyu Liu, "Computing Stable Skeletons with Particle Filters", the 10th Pacific Rim Int. Conf. on Artificial Intell. (PRICAI), Hanoi, Vietnam, 2008. (AR = 21.3% for Oral Presentations).PDF

[3].Quan-Nan Li, Xiang Bai, and Wen-Yu Liu, "Skeletonization of Gray-Scale Images from Incomplete Boundaries", IEEE International Conference on Image Processing (ICIP'08), San Diego, USA, 2008.PDF

[4].Xingwei Yang, Xiang Bai, Longin Jan Latecki, Zhuowen Tu, "Improving Shape Retrieval by Learning Graph Transduction "：(Best Ever Retrieval Rate of 91% on MPEG-7!) , ECCV, to appear in 2008.PDF

[5]. Hairong Liu, Longin J Latecki, and Wenyu Liu, "A Unified Curvature Definition for Regular, polygonal, and Digital Planar Curves",Editorial Manager(tm) for International Journal of Computer Vision PDF

[6]. Xiang Bai, Xingwei Yang, and Longin Jan Latecki, " Detection and Recognition of Contour Parts Based on Shape Similarity", Pattern Recognition (PR), to appear in 2008. PDF

[7]. Xiang Bai, Longin Jan Latecki, and Wenyu Liu, “Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution”, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), 29(3): 449-462, 2007. PDF
Matlab code for skeleton pruning:
http://www.cis.temple.edu/~latecki/Programs/skeletonPruning07.htm

[8]. Xiang Bai, and Longin Jan Latecki, “Path Similarity Skeleton Graph Matching”, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), to appear. PDF

[9]. HaiRong Liu, Longin Jan Latecki, Wenyu Liu, Xiang Bai. Visual Curvature, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2007, CVPR 2007. PDF

[10]. Xiang Bai, and Longin Jan Latecki, “Discrete Skeleton Evolution”, International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Aug. 2007, oral presentation (AR = 15% for Oral Presentations). PDF

[11]. Longin Jan Latecki, Quan-Nan Li, Xiang Bai, and Wenyu Liu, “Skeletonization Using SSM of the Distance Transform”, IEEE International Conference on Image Processing (ICIP), Sep. 2007. (AR = 45%) PDF

[12]. Xiang Bai, Longin Jan Latecki, and Wenyu Liu, “Skeleton Pruning by Contour Partition”, 13th International Conference on Discrete Geometry for Computer Imagery (DGCI), pp: 567-579, October 2006. (AR = 28% for Oral Presentations) PDF

[13]. Wenyu Liu, Xiang Bai, and Guang-Xi Zhu, “A skeleton-growing Algorithm Based on Boundary Curve Evolution”, ACTA AUTOMATICA SINICA, 32(2): 255-262, 2006. (In Chinese)

[14]. N. Adluru, L.J. Latecki, R. Lakaemper, T. Young, X. Bai, and A. Gross. Contour Grouping Based on Local Symmetry. 11th IEEE Int. Conf. on Computer Vision (ICCV), Rio de Janeiro, Brazil, October 2007. PDF

[15]. Xiang Bai, Xing-Wei Yang, and Longin Jan Latecki, “Shape Classification Based on Skeletons”, International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), to appear.

[16]. Xing-Wei Yang, Xiang Bai, and Longin Jan Latecki, “Shape Classification Based on Skeleton Path Similarity”, International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Aug. 2007, oral presentation (AR = 15% for Oral Presentations).PDF

[17]. Quan Zhou, Wenyu Liu, Cunhui Chui, A Novel Macroblock Layer Rate Control with Low Bit Rate, Journal of Image and Graphic, 9(8), 2007.

[18]. Xi Geng, Wenyu Liu, Hairong Liu, Force Field Based Expression for 3D Shape Retrieval, 12th International Conference on Human-Computer Interaction, 2007, (LNCS). PDF

Useful Link

Instructions of Publications

Computer Vision Homepage @CMU
http://www.cs.cmu.edu/~cil/vision.html

Prof. Dr. Longin Jan Latecki @Temple University
http://www.cis.temple.edu/~latecki/

Prof. Dr. Song Chun Zhu @UCLA
http://www.stat.ucla.edu/~sczhu/

Prof. Dr. Zhuowen Tu @UCLA
http://www.loni.ucla.edu/~ztu/index.html

Prof. Dr. Alan Yuille @UCLA
http://www.stat.ucla.edu/~yuille/

Dr. Lei Zhang @MSRA
http://research.microsoft.com/users/leizhang/

  Members

There are 7 members in our current group:

Current PhD students：

Team Leader: Xiang Bai (2005 Supervisor: Wenyu Liu, Zhuowen Tu and L.J. Latecki)
Microsoft Fellowship 2007, Joint PhD student at UCLA supervised by Zhuowen Tu
Shape Representation, Shape Matching and Classification, Object Detection and Contour Grouping

Email: xiang.bai@gmail.com

Xingwei Yang (2006 Supervisor: L.J. Latecki)
Shape Matching and Classification)

Email: xingwei.yang@temple.edu

Jun Chen (2006 Supervisor: Wenyu Liu)
Shape Matching and Classification)

Email: chenjun71983@163.com

Current MSc students：

Yu Zhou 2009
Shape Analysis, Machine Learning

Email: zhouyu.hust@gmail.com

Xinggang Wang 2009
Object Detection and Recognition

Email: wxghust@gmail.com

Current undergraduate students：

Bo Wang
Chunyuan Li
Qiao Zhang