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Zhuowen
Tu
Assistant
Professor
Department
of Neurology and Department of Computer Science
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Zhuowen Tu's
research has
been on the interface of medical imaging, machine learning, statistical
modeling/computing, and computer vision. More specifically, his
interest is in studying statistical and computational frameworks for
discriminative models, generative models, and their relationships; the
applications he works are natural/medical image segmentation, object
detection/recognition, shape analysis, and manifold learning.
Representative
Work:
| Jun-Yan Zhu, Jiajun Wu, Yichen
Wei, Eric Chang, and Zhuowen Tu, "Unsupervised Object
Class Discovery via Saliency-Guided Multiple Class Learning", CVPR
2012. Yan Xu*, Jun-Yan Zhu*, Eric Chang, and Zhuowen Tu, "Multiple Clustered Instance Learning for Histopathology Cancer Image Segmentation, Clustering, and Classification", CVPR 2012. |
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Bo Wang and Zhuowen Tu,
"Affinity Learning via Self-diffusion for Image Segmentation and
Clustering", Proc. of CVPR 2012. Jiayan Jiang, Bo Wang, and
Zhuowen Tu, "Unsupervised Metric Learning by
Self-Smoothing Operator", Proc. of ICCV 2011. (matlab source code) |
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Piotr Dollar, Boris
Babenko, Serge Belongie, Pietro Perona, and Zhuowen Tu, Multiple
Component Learning for Object Detection, Proc. of
ECCV 2008. |
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Xiang Bai, Xingwei Yang,
Longin Jan Latecki, Wenyu Liu, and Zhuowen Tu, Learning Context Sensitive Shape
Similarity by Graph Transduction, IEEE Trans. on
Pattern Analysis and Machine Intelligence, 32(5), pp. 861-874, 2010. Download
the demo code. |
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Quannan Li, Jingdong Wang, David
Wipf, and Zhuowen Tu, "Fixed-Point
Model for Structured Labeling", International Conference on Machine
Learning (ICML), Atlanta, June, 2013. Zhuowen Tu, Auto-context and Its application to
High-level Vision Tasks, Proc. of IEEE Computer
Vision and Pattern Recognition (CVPR), 2008. |
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Zhuowen Tu, Learning Generative Models via Discriminative Approaches, Proc. of IEEE Computer Vision and Pattern Recognition (CVPR), 2007. |
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Zhuowen Tu,
Katherin Narr, Piotr Dollar, Iov Dinov, Paul Thompson, Arthur Toga, Brain Anatomical Structure Segmentation
by Hybrid Discriminative/Generative Models, Transactions
on Medical Imaging, vo. 27, no. 4, pp.495-508, 2008. |
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Zhuowen Tu, Probabilistic Boosting-Tree: Learning
Discriminative Models for Classification, Recognition, and Clustering,
10th IEEE International Conf. on Computer Vision (ICCV),
Oct. 2005. |
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Zhuowen Tu and Alan Yuille, Shape Matching and Recognition--Using Generative Models and Informative Features, 8th European Conf. on Computer Vision (ECCV), 2004. Zhuowen Tu, Songfeng Zheng, and Alan Yuille, Shape matching and registration by data-driven EM, Computer Vision and Image Understanding, vol. 109, pp. 290-304, 2008. |
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Zhuowen Tu,
Xiangrong Chen, Alan Yuille,
and Song-Chun Zhu, Image Parsing: Unifying Segmentation,
Detection, and Object Recognition, International Journal of Computer Vision,
vol. 63, no. 2, pp. 113-140, 2005. |
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Zhuowen Tu
and Song-Chun Zhu, Image
Segmentation by Data-Driven Markov Chain Monte Carlo,
IEEE Trans. on Pattern Analysis and Machine Intelligence,
Vol. 24, No. 5, May, 2002. |
Funding Supports:
NSF IIS_1216528
NSF IIS-0844566
ONR N000140910099
.