Stephen Gould
I'm a Fellow (Senior Lecturer) in the Research School of Computer Science in the College of Engineering and Computer Science at the Australian National University.

Email:

Phone:
+61-(0)2-6125-8642 (office)
+61-(0)408-879-963 (mobile)

Address:
RSISE, Room B227, Building 115,
The Australian National University,
Corner North and Daley Roads,
Acton, ACT, 2601
AUSTRALIA

News and Events


[ more ]
Research

I have broad interests in computer and robotic vision, machine learning, probabilistic graphical models, and optimization. My main research focus is on the application of machine learning techniques (specifically, conditional Markov random fields) to geometric and semantic scene understanding. I am also interested in seeing research get used. I collaborate with industry and have previously been involved in founding start-up companies.

Getting involved: I am always looking for motivated students who are interested in doing research with me. You can read some of my selected papers (below) to get a feel for the type of work that I do. I encourage students to contact me but please read the following before doing so:

All applications for PhD or Masters should come through the ANU applications system. Please check the above links for scholarship deadlines.
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All Publications [ show selected ]
(Google Scholar Profile)

Determining Interacting Objects in Human-Centric Activities via Qualitative Spatio-temporal Reasoning
Hajar Sadeghi Sokeh, Stephen Gould and Jochen Renz.
To appear in Proceedings of the Asian Conference on Computer Vision (ACCV), 2014.
[ pdf | bib ]
Superpixel Graph Label Transfer with Learned Distance Metric
Stephen Gould, Jiecheng Zhao, Xuming He and Yuhang Zhang.
In Proceedings of the European Conference on Computer Vision (ECCV), 2014.
[ pdf | code | bib ]
An Exemplar-based CRF for Multi-instance Object Segmentation
Xuming He and Stephen Gould.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
[ pdf | bib ]
Joint Semantic and Geometric Segmentation of Videos with a Stage Model
Buyu Liu, Xuming He and Stephen Gould.
In IEEE Winter Conference on Applications of Computer Vision (WACV), 2014.
[ pdf | bib ]
A Unified Graphical Models Framework for Automated Mitosis Detection in Human Embryos
Farshid Moussavi, Wang Yu, Peter Lorenzen, Jonathan Oakley, Daniel Russakoff and Stephen Gould.
In IEEE Transactions on Medical Imaging (TMI), 2014.
A shorter version of this paper appeared in IEEE International Symposium on Biomedical Imaging (ISBI), 2014.
[ pdf | bib ]
Efficient Extraction and Representation of Spatial Information from Video Data
Hajar Sadeghi Sokeh, Stephen Gould and Jochen Renz.
In Proceedings of the Twenty Third International Joint Conference on Artificial Intelligence (IJCAI), 2013.
[ pdf | bib ]
Discriminative Learning with Latent Variables for Cluttered Indoor Scene Understanding
Huayan Wang, Stephen Gould and Daphne Koller.
In Communications of the ACM, Research Highlights, 2013.
[ pdf | link | bib ]
A Multiple Model Probability Hypothesis Density Tracker for Time-lapse Cell Microscopy Sequences
Seyed Hamid Rezatofighi, Stephen Gould, Ba-Ngu-Vo, Katarina Mele, William E. Hughes and Richard Hartley.
In International Conference on Information Processing in Medical Imaging (IPMI), 2013.
[ pdf | bib ]
A Framework for Generating Realistic Synthetic Sequences of Total Internal Reflection Flourescence Microscopy Images
Seyed Hamid Rezatofighi, William T. E. Pitkeathly, Stephen Gould, Richard Hartley, Katarina Mele, William E. Hughes and James G. Burchfield.
In Proceedings of the International Symposium on Biomedical Imaging (ISBI), 2013.
[ pdf | code | bib ]
DARWIN: A Framework for Machine Learning and Computer Vision Research and Development
Stephen Gould.
In Journal of Machine Learning Research (JMLR), 2012.
[ pdf | code | mloss | bib ]
Towards Unsupervised Semantic Segmentation of Street Scenes From Motion Cues
Hajar Sadeghi Sokeh and Stephen Gould.
In Proceedings of the International Conference on Image and Vision Computing New Zealand (IVCNZ), 2012.
[ pdf | bib ]
A Noise Tolerant Watershed Transformation with Viscous Force for Seeded Image Segmentation
Di Yang, Stephen Gould and Marcus Hutter.
In Proceedings of the Asian Conference on Computer Vision (ACCV), 2012.
[ pdf | bib ]
PatchMatchGraph: Building a Graph of Dense Patch Correspondences for Label Transfer
Stephen Gould and Yuhang Zhang.
In Proceedings of the European Conference on Computer Vision (ECCV), 2012.
[ pdf | code | polo dataset (32MB) | stanford dataset (15MB) | bib ]
On Learning Higher-Order Consistency Potentials for Multi-class Pixel Labeling
Kyoungup Park and Stephen Gould.
In Proceedings of the European Conference on Computer Vision (ECCV), 2012.
[ pdf | bib ]
Application of the IMM-JPDA Filter to Multiple Target Tracking in Total Internal Reflection Fluorescence Microscopy Images
Seyed Hamid Rezatofighi, Stephen Gould, Richard Hartley, Katarina Mele and William E. Hughes.
In Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2012.
[ pdf | bib ]
Multiclass Pixel Labeling with Non-Local Matching Constraints
Stephen Gould.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
[ pdf | bib ]
Simultaneous Multi-class Pixel Labeling over Coherent Image Sets
Paul Rivera and Stephen Gould.
In Digital Image Computing: Techniques and Applications (DICTA), 2011.
[ pdf | bib ]
Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields
Stephen Gould.
In Proceedings of the International Conference on Machine Learning (ICML), 2011.
[ pdf | code | slides (.pdf) | bib ]
Discriminative Learning with Latent Variables for Cluttered Indoor Scene Understanding
Huayan Wang, Stephen Gould and Daphne Koller.
In Proceedings of the European Conference on Computer Vision (ECCV), 2010.
[ pdf | bib ]
A Unified Contour-Pixel Model for Segmentation
Ben Packer, Stephen Gould and Daphne Koller.
In Proceedings of the European Conference on Computer Vision (ECCV), 2010.
[ pdf | bib ]
Probabilistic Models for Region-based Scene Understanding
Stephen Gould.
Ph.D. Thesis, Stanford University, June 2010.
[ pdf | archive | bib ]
Accelerated Dual Decomposition for MAP Inference
Vladimir Jojic, Stephen Gould and Daphne Koller.
In Proceedings of the International Conference on Machine Learning (ICML), 2010.
[ pdf | bib ]
Single Image Depth Estimation from Predicted Semantic Labels
Beyang Liu, Stephen Gould and Daphne Koller.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
[ pdf | data (.tar.gz) | bib ]
Region-based Segmentation and Object Detection
Stephen Gould, Tianshi Gao and Daphne Koller.
In Advances in Neural Information Processing Systems (NIPS), 2009.
[ pdf | bib ]
Decomposing a Scene into Geometric and Semantically Consistent Regions
Stephen Gould, Rick Fulton and Daphne Koller.
In IEEE International Conference on Computer Vision (ICCV), 2009.
[ pdf | slides (.pdf) | inference (.wmv) | data (.tar.gz) | bib ]
Alphabet SOUP: A Framework for Approximate Energy Minimization
Stephen Gould, Fernando Amat and Daphne Koller.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
[ pdf | erratum | poster | bib ]
High-Accuracy 3D Sensing for Mobile Manipulation: Improving Object Detection and Door Opening
Morgan Quigley, Siddharth Batra, Stephen Gould, Ellen Klingbeil, Quoc V. Le, Ashley Wellman and Andrew Y. Ng.
In IEEE International Conference on Robotics and Automation (ICRA), 2009.
[ pdf | videos | bib ]
Cascaded Classification Models: Combining Models for Holistic Scene Understanding
Geremy Heitz, Stephen Gould, Ashutosh Saxena and Daphne Koller.
In Advances in Neural Information Processing Systems (NIPS), 2008.
[ pdf | bib ]
Learning Bounded Treewidth Bayesian Networks
Gal Elidan and Stephen Gould.
In Advances in Neural Information Processing Systems (NIPS), 2008.
A longer version of this paper also appears in Journal of Machine Learning Research (JMLR), 2008.
[ pdf (nips) | pdf (jmlr) | bib ]
Integrating Visual and Range Data for Robotic Object Detection
Stephen Gould, Paul Baumstarck, Morgan Quigley, Andrew Y. Ng and Daphne Koller.
In ECCV workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications (M2SFA2), 2008.
[ pdf | bib ]
Projected Subgradient Methods for Learning Sparse Gaussians
John Duchi, Stephen Gould and Daphne Koller.
In Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI), 2008.
[ pdf | bib ]
Multi-Class Segmentation with Relative Location Prior
Stephen Gould, Jim Rodgers, David Cohen, Gal Elidan and Daphne Koller.
In International Journal of Computer Vision (IJCV), 2008.
[ pdf | bib ]
STAIR: The STanford Artificial Intelligence Robot Project
Andrew Y. Ng, Stephen Gould, Morgan Quigley, Ashutosh Saxena and Eric Berger.
In Learning Workshop, Snowbird, 2008.
[ project ]
Peripheral-Foveal Vision for Real-time Object Recognition and Tracking in Video
Stephen Gould, Joakim Arfvidsson, Adrian Kaehler, Benjamin Sapp, Marius Meissner, Gary Bradski, Paul Baumstarck, Sukwon Chung and Andrew Y. Ng.
In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI), 2007.
[ pdf | bib ]
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Software

The following lists some large software libraries that I have developed. For reference implementations of the algorithms described in my work see the links next to the relevant paper in my publications list.

Darwin
A C++ framework for machine learning and computer vision research. The framework includes a wide range of standard machine learning and graphical models algorithms as well as reference implementations for many of the algorithms described in the publications above. The code is released under the BSD license. If you are interested in contributing to this codebase then please email me.
[ version 1.8 | documentation | video help | mloss | github ]

The STAIR Vision Library
A platform independent C++ toolkit for computer vision research (building on top of OpenCV). The library also includes many machine learning and probabilistic graphical models algorithms. We have released the code under the BSD license. Developed while I was at Stanford University, this library is no longer supported---much of its functionality, however, is available in the Darwin framework described above.
[ wiki | doc | sourceforge ]
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Professional Activities

Workshops, Conferences, and Journals
I have regularly served as program committee member or reviewer for the following conferences and journals: CVPR, ECCV, ICCV, ICML, IEEE PAMI, IEEE TIP, IJCV, JMLR, NIPS, RSS, UAI and others.

Selected Invited Talks and Tutorials

Teaching

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Issued Patents

US 7,725,312. Transcoding method and system between CELP-based speech codes with externally provided status.
US 7,411,418. Efficient representation of state transition tables.
US 7,301,792. Apparatus and method of ordering state transition rules for memory efficient, programmable, pattern matching finite state machine hardware.
US 7,219,319. Apparatus and method for generating state transition rules for memory efficient programmable pattern matching finite state machine hardware.
US 7,184,953. Transcoding method and system between CELP-based speech codes with externally provided status.
US 7,180,328. Apparatus and method for large hardware finite state machine with embedded equivalence classes.
US 7,082,044. Apparatus and method for memory efficient, programmable, pattern matching finite state machine hardware.
US 6,829,579. Transcoding method and system between CELP-based speech codes.
AU 2004222859. A method for developing algorithms.
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Useful Links
Google Scholar