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Cheong Loong Fah
Associate Professor
E-mail: eleclf@nus.edu.sg Phone:(65) 6516-2290 Fax:(65) 6779-1103

PhD (CS), U of Maryland at College Park; 1996
BEng (Hons) NUS; 1990


Department of Electrical and Computer Engineering
Faculty of Engineering
National University of Singapore 

 

 

 

My Research Programme

Many real world vision tasks such as motion segmentation, large scale scene reconstruction, and object/scene classification remain challenging research problems. This is not surprising, considering that more than half of the neocortex is involved in visual processing. To solve these vision problems robustly, we envisage that it requires a concerted research effort in integrating the different vision modules together, as well as further advancing our fundamental understanding of the individual vision modules.

 

I am interested to investigate the feedback and lateral links that exist in complex vision problems. For instance, the segmentation problem will need to combine a broad range of technical advances in computation of 3D surfaces, knowledge about natural scene statistics and gestalt laws, and expertise in advanced mathematical techniques such as level set, graph cut, MRF-based learning. It provides an opportunity for psychophysical, computational and mathematical studies that will extend our understanding of the processes underlying the interactions between various vision modules, as well as the incorporation of environmental constraints to enhance the performance in dynamic, real-world situations.

 

In the past decade, my research has revolved around the central question of space perception arising from motion cues, otherwise known as the structure from motion (SFM) problem. I approach this problem mainly from the computational perspective but also study the psychophysical implication and some applied aspects (see the links in the next few section). It remains an active area of my research. Now, I am using sparsity-based techniques to address various motion problems such as 3D motion segmentation, as well as change detection amidst scenes with complicated dynamic behavior such as swaying trees and undulating waves.

 

For prospective research students, I am looking for someone who is really keen to understand the visual processes involved in human vision, and feels excited to build a robust system that can function in the real world. Preferably, the student must have an adequate level of mathematical sophistication, as the field of computer vision is currently going through a crucial mutation, requiring more and more mathematical skills such as PDE, differential geometry, functional analysis, etc.. Interested students with EE, CS, or applied mathematics background are welcome to contact me.

 


Recent Research Topics
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SCAMS: Simultaneous Clustering and Model Selection
Zhuwen Li, Loong-Fah Cheong, and Steven Zhiying Zhou.
IEEE Conference on Computer Vision and Patten Recognition (CVPR), 2014
[PDF (154KB)][Matlab Code ]
1. Using an indicator matrix formulation to highly constrain solution space, allowing us to repair imperfections in the affinity matrix, 2. exploiting the particular structure present in inner optimization subproblems, allowing us to enforce the rank & L0 norm constraints directly
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Practical Matrix Completion and Corruption Recovery using Proximal Alternating Robust Subspace Minimization
Yu-Xiang Wang, Choon Meng Lee, Loong-Fah Cheong, Kim-Chuan Toh.
International Journal of Computer Vision, 2014
[PDF (6MB)][Matlab Code]
- Can handle high % of missing data, non-random support, noise and gross corruptions.
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Block-sparse RPCA for Salient Motion Detection
Zhi Gao, Loong-Fah Cheong, and Yu-Xiang Wang.
IEEE Transaction on Pattern Analysis and Machine Intelligence, 2014
[PDF (1.6MB)][Matlab Code ][Video Demo 1 (69MB) ][Video Demo 2 (21MB) ]
- State-of-the-art results in 2 recent change detection benchmarks
- Can handle illumination change, bad weather, background motion, camera jitter, disparate scale
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Perspective Motion Segmentation via Collaborative Clustering
Zhuwen Li, Jiaming Guo, Loong-Fah Cheong, and Steven Zhiying Zhou.
IEEE International Conference on Computer Vision (ICCV), 2013 (Oral)
[PDF (2MB)][62-clip Dataset Download (84 MB)]
- Can handle perspective effects, missing data, model selection, and yet retain elegance of formulation.
- State-of-the-art results in handling the preceding challenges
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Consistent Foreground Co-segmentation
Jiaming Guo, Loong-Fah Cheong, Robby T. Tan and Steven Zhiying Zhou.
Asian Conference on Computer Vision (ACCV), 2014
[PDF (9MB)][Project page and CFViCS database download]
- Can handle foreground with variegated appearance, moving nonrigidly or consisting of multiple interacting entities (e.g. a mating pair of birds).
- Can handle cluttered background and remove extraneous objects momentarily moving together.
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Video Co-segmentation for Meaningful Action Extraction
Jiaming Guo, Zhuwen Li, Loong-Fah Cheong, and Steven Zhiying Zhou.
IEEE International Conference on Computer Vision (ICCV), 2013
[PDF (18MB)][Video Demo (53MB)]
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Diminished Reality using Appearance and 3D Geometry of Internet Photo Collections
Zhuwen Li, Yuxi Wang, Jiaming Guo, Loong-Fah Cheong, and Steven Zhiying Zhou.
IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2013
[PDF (12MB)][Video Demo (25MB)]
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Quasi-Parallax for Nearly Parallel Frontal Eyes --a possible role of binocular overlap during rapid locomotion
Loong-Fah Cheong, Zhi Gao.
International Journal of Computer Vision, 2013
[PDF (2MB)]
- We look at whether binocular overlap is necessarily leveraged in terms of stereoscopic depth recovery.
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Active Visual Segmentation
Ajay K. Mishra, Yiannis Aloimonos, Loong-Fah Cheong, and Ashraf A. Kassim.
IEEE Transaction on Pattern Analysis and Machine Intelligence, 2012
[PDF (2.6MB)][Matlab Code]
- Multiple fixation-based segmentation that can handle thin structures and disparate scales.



SAMPLE PUBLICATION


Computational Domain

Geometry of Distorted Visual Space . Int'l Journal of Computer Vision, 32(3), pp 195-212, 1999. © 1999 by Kluwer academic

Error in Depth Reconstruction . Int'l Journal of Computer Vision, 44(3), pp 199-217, Aug 2001. © 2001 by Kluwer academic

Behaviour of SFM algorithms . Int'l Journal of Computer Vision, 51(2), 111-137, 2003. © 2003 Kluwer academic

Depth distortion under calibration uncertainty, Computer Vision and Image Understanding, Volume 93, Issue 3 , March 2004, Pages 221-244.

How do we Perceive Depths from Motion Cues in the Movies: A Computational Account, Journal of the Optical Society of America A: Optics, Image Science, and Vision, 2008.

Linear Quasi-Parallax SfM using Laterally-placed Eyes, International Journal of Computer Vision, Volume 84, Number 1 / August, 2009, pg 21-39.

Hierarchical Spatio-Temporal Context Modeling for Action Recognition, CVPR09. 

 

When Discrete Meets Differential — Assessing the Stability of Structure from Small Motion, International Journal of Computer Vision, vol. 86, nos 1, pp. 87-110, 2010.

 

Error Characteristics of SFM with Erroneous Focal Length, Computer Vision and Image Understanding, 115, No.1, (Jan 2011) 16–30.

 

Smoothly Varying Affine Stitching, CVPR 2011, Jun 20-25, Oral presentation

 

Active Visual Segmentation,  IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), Vol 34, No. 2, p639-653, April 2012.

 

Simultaneous Camera Pose and Correspondence Estimation with Motion Coherence,  International Journal of Computer Vision, 96(2): 145-161 2012

 

Quasi-Parallax for Nearly Parallel Frontal Eyes --a possible role of binocular overlap during rapid locomotion, accepted for publication in International Journal of Computer Vision, June 2012.

 

Seeing Double Without Confusion: Structure-from-Motion in Highly Ambiguous Scenes, CVPR 2012.

 

Block-sparse RPCA for Consistent Foreground Detection, ECCV2012.

 

Diminished Reality using Appearance and 3D Geometry of Internet Photo Collections, ISMAR 2013.

 

Minimal Basis Facility Location for Subspace Segmentation, ICCV2013.

 

Perspective Motion Segmentation via Collaborative Clustering, ICCV2013. Oral presentation

 

Video Co-segmentation for Meaningful Action Extraction, ICCV2013.

 

Semantic Segmentation without Annotating Segments, ICCV2013.

 

SCAMS: Simultaneous Clustering and Model Selection, CVPR2014.

 

Psychophysical Domain

Slant and Tilt Perception: A computational and psychophysical study . “Lecture Notes in Computer Science” Vol 1843, 2000. © 2000 by Springer-Verlag

Absolute distance perception during in-depth head movement: Calibrating optic flow with extra-retinal information. Vision Research, 42(16), pp. 1991-2003, 2002.

The visual perception of plane tilt from motion in small field and large field: psychophysics and theory, Vision Research, Volume 46, Issue 20, October 2006, Pages 3494-3513.

 

Others

Scene-based shot change detection . Computer Vision and Image Understanding, 79: (2) 224-235 Aug 2000.  © 2000 by ACADEMIC PRESS  

 

Establishment Shot Detection Using Qualitative Motion. IEEE Conference on Computer Vision and Pattern Recognition, June 18 - 20, 2003, Madison, Wisconsin, Volume II, p. 85-90.

 

Framework for synthesizing semantic-level indexes. Multimedia Tools and Applications 20(2): 135-158; Jun 2003.

 

Synergizing Spatial and Temporal Texture. IEEE Transactions on Image Processing 11(10), pp. 1179-1191, 2002.

 

Addressing the problems of Bayesian Network classification, IEEE Transactions on Knowledge and Data Engineering, Volume 16 ,  Issue 2,  February 2004, Pages: 230 – 244.

 

Affective Understanding in Film , IEEE Transactions on Circuits and Systems for Video Technology, Volume: 16  Issue: 6  June 2006. Page(s): 689- 704.

 

A Taxonomy of Directing Semantics for Film Shot Classification, IEEE Transactions on Circuits and Systems for Video Technology, Volume 19, No. 10, October 2009, pp. 1529-1542.

 

 

 

 My Teaching

·        EE6901 3D Vision & EE4212 Computer Vision, the former a Ph.D. level course and the latter a fourth year course.

·        Also leading a seminar class EE6903 Advanced Models Of Biological Perception and EE6733 Advanced Topics on Vision and Machine Learning.