f-zhou.com

Feng Zhou

Is proposed for better optimizing and understanding graph matching problems. Is proposed for temporally aligning multi-modal sequences. Is proposed to temporally cluster human behavior. Is proposed to discover facial events from video of naturally occurring facial behavior. Is proposed to robustly localize facial landmarks on image. Generates low-frame-rate video from a high-frame-rate input using Video Saliency. Time Mapping and Video Saliency. Is proposed for detecting and tracking humans in videos.

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Carnegie Mellon University

Feng Zhou

2304 ●●●●● Ave.

Pit●●●rgh , PA, 15217

US

1.41●●●●8529

zh●●●●@gmail.com

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Carnegie Mellon University

Feng Zhou

2304 ●●●●● Ave.

Pit●●●rgh , PA, 15217

US

1.41●●●●8529

zh●●●●@gmail.com

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Carnegie Mellon University

Feng Zhou

2304 ●●●●● Ave.

Pit●●●rgh , PA, 15217

US

1.41●●●●8529

zh●●●●@gmail.com

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Feng Zhou | f-zhou.com Reviews

<META> DESCRIPTION

Is proposed for better optimizing and understanding graph matching problems. Is proposed for temporally aligning multi-modal sequences. Is proposed to temporally cluster human behavior. Is proposed to discover facial events from video of naturally occurring facial behavior. Is proposed to robustly localize facial landmarks on image. Generates low-frame-rate video from a high-frame-rate input using Video Saliency. Time Mapping and Video Saliency. Is proposed for detecting and tracking humans in videos.

<META> KEYWORDS

1 featured

2 publications

3 softwares

4 factorized graph matching

5 graph matching

6 canonical time warping

7 temporal alignment

8 aligned cluster analysis

9 temporal clustering

10 an unsupervised system

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featured,publications,softwares,factorized graph matching,graph matching,canonical time warping,temporal alignment,aligned cluster analysis,temporal clustering,an unsupervised system,facial event discovery,exemplar based graph matching,time mapping

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Feng Zhou | f-zhou.com Reviews

https://f-zhou.com

Is proposed for better optimizing and understanding graph matching problems. Is proposed for temporally aligning multi-modal sequences. Is proposed to temporally cluster human behavior. Is proposed to discover facial events from video of naturally occurring facial behavior. Is proposed to robustly localize facial landmarks on image. Generates low-frame-rate video from a high-frame-rate input using Video Saliency. Time Mapping and Video Saliency. Is proposed for detecting and tracking humans in videos.

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f-zhou.com

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Temporal Clustering

http://www.f-zhou.com/tc.html

Temporal Clustering of Human Behavior. Decomposing a walking sequence into 4 clusters of movements. Fernando De la Torre. Jessica K. Hodgins. We pose the problem of learning motion primitives as a temporal clustering one, and derive aligned cluster analysis (ACA). And hierarchical aligned cluster analysis (HACA). ACA find a partition of a given multi-dimensional time series into m disjoint segments, such that each segment belongs to one of k clusters. ACA combines. Dynamic time alignment kernel. Download...

2

Graph Matching

http://www.f-zhou.com/gm.html

Matching two human bodies with 5 and 4 features. Our method simultaneously estimates the correspondence and a smooth non-rigid transformation between shapes. Our method is able to factorize the 20-by-20 pairwise affinity matrix as Kronecker product. Of six smaller matrices. The first two groups of matrices of size 5-by-6 and 4-by-10 encode the structure of each of the graphs. The last two matrices encode the affinities for nodes (5-by-4) and edges (16-by-10). Fernando De la Torre. The factorization enabl...

3

Facial Events Discovery

http://www.f-zhou.com/au.html

Selected video frames of unposed facial behavior from three participants. Different colors and shapes represent dynamic events discovered by unsupervised learning: smile (green circle) and lip compressor (blue hexagons). Dashed lines indicate correspondences between persons. Fernando De la Torre. Jeffrey F. Cohn. And a multi-subject correspondence algorithm for matching expressions. We use a variety of video sources: posed facial behavior (Cohn-Kanade database), unscripted facial behavior (RU-FACS da...

4

Publications

http://www.f-zhou.com/pub.html

My Google Scholar Profile. Spatio-temporal Matching for Human Pose Estimation in Video. IEEE Transactions on Pattern Analysis and Machine Intelligence ( PAMI. F Zhou and F. De la Torre. IEEE Transactions on Pattern Analysis and Machine Intelligence ( PAMI. F Zhou and F. De la Torre. Generalized Canonical Time Warping. IEEE Transactions on Pattern Analysis and Machine Intelligence ( PAMI. F Zhou and F. De la Torre. Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion. IEEE Confere...

5

About Me

http://www.f-zhou.com/bio.html

Zhejiang University, Bachelor Majored in Computer Science. ACM/ICPC Team, Member Attending Programming Contests. Shanghai Jiao Tong University, Master Majored in Computer Science. BCMI Lab, Staff Working on Ensemble Learning. Humansensing Lab, Staff Working on Human Behavior Analysis, Facial Expression Analysis, Feature Matching. Carnegie Mellon University, Master Majored in Robotics. Carnegie Mellon University, PhD Majored in Robotics. NEC Lab Fine-Grained Recognition. Group, NEC Lab. And Time Mapping a...

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humansensing.cs.cmu.edu

People | Human Sensing Laboratory

http://humansensing.cs.cmu.edu/people

Skip to main content. Http:/ www.cs.cmu.edu/ ftorre/. Fernando de la Torre. Welcome to the Human Sensing Laboratory (HSL). Our group focuses on providing computers with new computational tools to model and understand human behavior. No less important is our commitment to educating students in the areas of machine learning and human sensing. 135 students have conducted research in the HSL in the last 8 years. We hope you have time to learn more about our research. Http:/ www.pitt.edu/ jeffcohn/. Http:/ ww...

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f-zhou.com

Feng Zhou

Is proposed for better optimizing and understanding graph matching problems. Is proposed for temporally aligning multi-modal sequences. Is proposed to temporally cluster human behavior. Is proposed to discover facial events from video of naturally occurring facial behavior. Is proposed to robustly localize facial landmarks on image. Generates low-frame-rate video from a high-frame-rate input using Video Saliency. Time Mapping and Video Saliency. Is proposed for detecting and tracking humans in videos.

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