5000 Forbes Ave, Pittsburgh, PA 15213
Advisor: Prof. Vijayakumar
Email: yzhiding AT andrew.cmu.edu
I am currently a Ph.D. candidate at the Department of Electrical and Computer Engineering, Carnegie Mellon University. Before that I obtained the M.Phil. degree from the Department of Electrical and Computer Engineering, The Hong Kong University of Science and Technology.
My research interests mainly focus on Graph-Embedded Mode Seeking, Image Segmentation, Object Recognition and Scene Understanding.
I am the awardee of the 2009-2010/2011-2012 HKTIIT Post-Graduate Excellence Scholarships and Carnegie Institute of Technology Dean's Tuition Fellowship.
Ph.D., Electrical and Computer Engineering,
Carnegie Mellon University
Sep. 2012 - Jul. 2016 (Expected)
QPA: 4.0/4.0 (A) Advisor: Prof. Vijayakumar Bhagavatula
M.Phil., Electronic and Computer
Engineering, The Hong Kong University of Science and Technology Sep.
2009 - Aug. 2012
CGA: 3.93/4.3 (A) Advisor: Prof. Oscar C. Au
M. Sc., Electronic Engineering, The Hong
Kong University of Science and Technology
Sep. 2008 - Jun. 2009
CGA: 11.75/12 (A+) Advisor: Prof. Bertram E. Shi
B. Eng., Information Engineering (Talented Student Program), South China University of Technology Sep. 2005 - Jul. 2008
Zhiding Yu, “Graph Embedding and Arbitrarily Shaped Clustering for Unsupervised Image Segmentation,” Master of Philosophy Thesis, HKUST, 2012. [paper]
Zhiding Yu, Oscar C. Au, Ruobing Zou, Weiyu Yu and Jing Tian, “An Adaptive Unsupervised Approach toward Pixel Clustering and Color Image Segmentation,” Pattern Recognition, 2010, 43. [paper]
Simin Yu and Zhiding Yu, “A novel fifth-order hyperchaotic circuit and its research,” Acta Physica Sinica, 2008, 57 (11).
Zhiding Yu, Ang Li, Oscar C. Au and Chunjing Xu, “Bag of Textons for Image Segmentation via Soft Clustering and Convex Shift,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, Rhode Island, USA, 2012. [paper] [poster] (Acceptance Rate: 24%)
Wenxiu Sun, Oscar C. Au, Lingfeng Xu, Yujun Li, Wei Hu and Zhiding Yu, “Texture Optimization for Seamless View Synthesis Through Energy Minimization,” ACM Multimedia (ACM-MM) 2012, Nara, Japan. (Accpetance Rate: 30%)
Zhiding Yu, Oscar C. Au, Ketan Tang and Chunjing Xu, “Nonparametric Density Estimation on A Graph: Learning Framework, Fast Approximation and Application in Image Segmentation,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, USA, 2011. [paper] (Acceptance Rate: 22.5%)
Zhiding Yu, Chunjing Xu, Jianzhuang Liu, Oscar C. Au and Xiaoou Tang, “Automatic Object Segmentation from Large Scale 3D Urban Point Clouds through Manifold Embedded Mode Seeking,” ACM Multimedia (ACM-MM), Scottsdale, Arizona, USA, 2011. [paper] [poster] (Acceptance Rate: 30%)
Zhiding Yu, Oscar C. Au, Ketan Tang, Lingfeng Xu, Wenxiu Sun and Yuanfang Guo, “Towards Robust and Efficient Segmentation: An Approach based on Inter-Region Contour and Intra-Region Content Analysis,” IEEE International Conference on Multimedia & Expo (ICME), Barcelona, Spain, 2011. [paper] (Acceptance Rate: Top 15%)
Ketan Tang, Oscar C. Au, Lu Fang, Zhiding Yu, Yuanfang Guo, “Multi-scale Analysis of Color and Texture for Salient Object Detection,” IEEE International Conference of Image Processing (ICIP), Brussels, Belgium, 2011.
Ketan Tang, Lu Fang, Zhiding Yu, Yuanfang Guo and Oscar C. Au, “How Anti-Aliasing Filter Affects Image Contrast: An Analysis from Majorization Theory Perspective,” IEEE International Conference on Multimedia & Expo (ICME), Barcelona, Spain, 2011. (Acceptance Rate: 30%)
Wenxiu Sun, Oscar C. Au, Lingfeng Xu and Zhiding Yu, “Adaptive Depth Map Assisted Matting in 3D Video,” IEEE International Conference on Multimedia & Expo (ICME), Barcelona, Spain, 2011. (Acceptance Rate: 30%)
Chi Ho Yeung, Oscar C. Au, Ketan Tang and Zhiding Yu, “Compressing Similar Image Sets using Low Frequency Template Prediction,” IEEE International Conference on Multimedia & Expo (ICME), Barcelona, Spain, 2011. (Acceptance Rate: Top 15%)
Yuanfang Guo, Oscar Au, Ketan Tang, Lu Fang and Zhiding Yu, “Data Hiding in Dot Diffused Halftone Images,” International Workshop on Content Protection & Forensics (CPAF), held in conjunction with ICME, Barcelona, Spain, 2011.
Ketan Tang, Oscar C. Au, Lu Fang, Zhiding Yu and Yuanfang Guo, "Image Interpolation Using Autoregressive Model and Gauss-Seidel Optimization," International Conference on Image and Graphics (ICIG), USTC, Hefei, China, 2011.
Zhiding Yu, Oscar C. Au, Ketan Tang, Jiali Li, Lingfeng Xu and Xingyu Zhang, “Graph Segmentation Revisited: Detailed Analysis and Density Learning based Implementation," IEEE International Conference on Multimedia & Expo (ICME), Singapore, 2010. (Acceptance Rate: 30%)
|I am particularly interested in joint object recognition and segmentation. I am currently investigating some interesting ideas on this topic. Here are some premative results.|
The clear path detection method for car reversing intended to be developed in this project is basically three-folded: It consists 1. building clear path models using both offline learning and online learning, 2. false positive rejection by pedestrian and vehicle detectors, and 3. final inference of clear path labels.
Differs from traditional clear path detection methods, our method takes the advantage of available priors in a car reversing scenario, particularly with online training which can improve the classification accuracy. The algorithm will operate on the superpixel framework for speed up and better feature extraction purposes. Each superpixel will be assigned a label to indicate whether it is clear path or not.
A premitive result video is available here.
We present a novel framework for tree-structure embedded density estimation and its fast approximation for mode seeking. Given any undirected, connected and weighted graph, the density function is defined as a joint representation of the feature space and the distance domain on the graph’s spanning tree. Since the distance domain of a tree is a constrained one, mode seeking can not be directly achieved by traditional mean shift in both domain. we address this problem by introducing node shifting with force competition and its fast approximation. The new formulation of this problem can lead to many advantages and new characteristics in its application.
This work appears in CVPR 2011.
We present a system that can automatically segment objects in large scale 3D point clouds obtained from urban ranging images. The system consists of three steps: The first one involves a ground detection process that can detect relatively complex terrain and separate it from other objects. The second step superpixelizes the remaining objects to speed up the segmentation process. In the final step, a manifold embedded mode seeking method is adopted to segment the point clouds. Even though the segmentation of urban objects is a challenging problem in terms of accuracy and problem scale, our system can efficiently generate very good segmentation results. The proposed manifold learning effectively improves the segmentation performance due to the fact that continuous artificial objects often have manifold-like structures.
This work appears in ACM-MM 2011.
We give a brief introduction of nonparametric density estimation and the family of mode seeking methods under this framework. Regarding the limit of current methods, we propose several improvements. The major contribution is basically two fold:
1. We show that for mean shift with a linear kernel, each kernel shift can actually be formulated in a convex form. By doing this we further generalize it for several convex metrics - e.g., KL divergence and Jeffrey divergence - where it is difficult or impossible to clearly define a ”mean” for mode seeking.
2. We also introduce interactive learning to image segmentation which is a specific application of mode seeking algorithms. By adopting interactive segmentation through convex learning, better segmentation results can be achieved over segmentation with traditional mode seeking algorithms.
The project report is available here.
An improved version of this work later appeared in CVPR 2012.
Carnegie Institute of Technology Dean's Tuition Fellowship, Carnegie Mellon University, 2012
2011-2012 HKTIIT Post-Graduate Excellence Scholarship, Hong Kong University of Science and Technology, 2012.
2009-2010 HKTIIT Post-Graduate Excellence Scholarship, Hong Kong University of Science and Technology, 2010.
HKUST Postgraduate Studentship
National Third Prize, 2007 National English Contest for College Students, May 2007.
Academic Annual Comprehensive Award of Academic Year 2006 – 2007, SCUT.
National Third Prize, 2006 National English Contest for College Students, May 2006.
Triple-A Student and Scholarship of Academic Year 2005 – 2006, SCUT.
National Silver Prize, 19th China Adolescents Science Technology Invention Contest, 2004.
Provincial Second Prize, 19th Guangdong Juvenile Activities of Science & Technology, 2004.
Prof. Deyu MENG
Prof. Oscar C. AU
Prof. Jianzhuang LIU
Dr. Chunjing XU
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