Jason Chang


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Publications

Julian Straub, Jason Chang, Oren Freifeld, and John W. Fisher III
A Dirichlet Process Mixture Model for Spherical Data
International Conference on Artificial Intelligence and Statistics (AISTATS), San Diego, CA, USA May 2015. (Oral)
[ PDF ] - [ Supp. Material ] -   +BibTeX
Jason Chang and John W. Fisher III
Parallel Sampling of HDPs using Sub-Cluster Splits
Neural Information and Processing Systems (NIPS), Montreal, Quebec, Canada Dec 2014.
[ PDF ] - [ Supp. Material ] - [ Poster ] - [ Code ]   +BibTeX
Jason Chang, Randi Cabezas, and John W. Fisher III
Bayesian Nonparametric Intrinsic Image Decomposition
European Conference on Computer Vision (ECCV), Zurich, Switzerland, Sept 2014. (Oral)
[ PDF ] - [ Presentation ] - [ Code ] -   +BibTeX
Jason Chang and John W. Fisher III
Parallel Sampling of DP Mixture Models using Sub-Clusters Splits
Neural Information and Processing Systems (NIPS), Lake Tahoe, NV, USA, Dec 2013.
[ PDF ] - [ Supp. Material ] - [ Poster ] - [ Code ]   +BibTeX
Jason Chang and John W. Fisher III
Topology-Constrained Layered Tracking with Latent Flow
IEEE International Conference on Computer Vision (ICCV), Syndey, Australia, Dec 2013.
[ PDF ] - [ Supp. Material ] - [ Poster ] - [ Code ]   +BibTeX  
Jason Chang, Donglai Wei, and John W. Fisher III
A Video Representation Using Temporal Superpixels
IEEE Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA, June 2013.
[ PDF ] - [ Supp. Material ] - [ Poster ] - [ Code ]   +BibTeX  
Jason Chang and John W. Fisher III
Efficient Topology-Controlled Sampling of Implicit Shapes
IEEE International Conference on Image Processing (ICIP), Orlando, FL, USA, Sept 2012.
[ PDF ] - [ arXiv ] - [ Poster ] - [ Code ]   +BibTeX  
Jason Chang and John W. Fisher III
Efficient MCMC Sampling with Implicit Shape Representations
IEEE Computer Vision and Pattern Recognition (CVPR), Colorado Springs, USA, June 2011.
[ PDF ] - [ Supp. Material ] - [ Poster ] - [ BSDS Results ] - [ Code ]   +BibTeX   +Summary
Jason Chang and John W. Fisher III
Analysis of Orientation and Scale in Smoothly Varying Textures
IEEE International Conference on Computer Vision (ICCV), Kyoto, Japan, Sept 2009.
[ PDF ] - [ Supp. Material ] - [ Poster ]   +BibTeX   +Summary

Code

Please visit here to see code used in my publications.

Reports and Theses

  • J. Chang, "Sampling in Computer Vision and Bayesian Nonparametric Mixtures", Ph.D. Thesis. MIT, Cambridge, MA June. 2014. [ PDF ]
  • J. Chang and J. W. Fisher III, "Efficient topology-controlled sampling of implicit shapes," May 2012, arXiv:1205.3766v1 [cs.CV]. [ PDF ]
  • J. Chang, "Extracting Orientation and Scale from Smoothing Varying Textures with Application to Segmentation", S.M. Thesis. MIT, Cambridge, MA Sept. 2009. [ PDF ]
  • Discrete-Time Signal Processing Project 1 - Selected for Solutions (2007) [ PDF ]
  • Discrete-Time Signal Processing Project 2 (2007) [ PDF ]
  • Undergraduate Senior Design Report (2007) [ PDF ]
  • Fast Hierarchical Filtered Back Projection - Digital Imaging Final Project (2007) [ PDF ]

Presentations

  • A Video Representation using Temporal Superpixels - ECCV IWVS Invited Short Talk (2014)
  • Bayesian Nonparametric Intrinsic Image Decomposition - ECCV Oral (2014)
  • Modeling Textures and Computing Statistics of Object Shapes in Images - BU Image and Video Computer Group Seminar (2011)
  • Inferring Relative Seismic Age - Stochastic Systems Group Seminar (2011)
  • Efficient MCMC Sampling with Implicit Shape Representations - CSAIL Computer Vision Seminar (2011) [ PPT ] [ PDF ]
  • Analysis of Smoothly Varying Textures - Research Qualifying Exam (2010) [ PDF ]
  • Analysis of Smoothly Varying Textures Applied to Segmentation - Automatic Target Recognition Center Workshop* (2009) [ PPT ]
  • Texture Based Image Segmentation - Stochastic Systems Group Seminar (2008) [ PPT ] [ PDF ]
  • Texture Based Image Segmentation - Airforce Sponsors Visit (2008) [ PDF ]
  • Music Search Engine - Undergraduate Senior Design Presentation (2007) [ PPT ] [ PDF ]
  • Fast Hierarchical Filtered Back Projection - Digital Imaging Final Project (2007) [ PPT ] [ PDF ]

Posters

  • MCMC Sampling in HDPs using Sub-Clusters - NIPS, Montreal, Canada (2014) [ PDF ]
  • Bayesian Nonparametric Intrinsic Image Decomposition - ECCV, Zurich, Switzerland (2014) [ PDF ]
  • Parallel Sampling of DP Mixture Models using Sub-Clusters Splits - NIPS, Lake Tahoe, NV (2013) [ PDF ]
  • Topology-Constrained Layered Tracking with Latent Flow - ICCV, Sydney, AU (2013) [ PDF ]
  • A Video Representation Using Temporal Superpixels - CVPR, Portland, OR (2013) [ PDF ]
  • Efficient Topology-Controlled Sampling of Implicit Shapes - ICIP, Orlando, FL (2012) [ PDF ]
  • Efficient MCMC Sampling with Implicit Shape Representations - CVPR, Colorado Springs, CO (2011) [ PDF ]
  • Analysis of Orientation and Scale in Smoothly Varying Textures - ICCV, Kyoto, Japan (2009) [ PDF ]
  • Analysis of Smoothly Varying Textures Applied to Segmentation - ATRC Workshop, Dayton, OH (2009) [ PDF ]
  • Modeling Slowly Varying Textures - CSAIL Industry Affiliates Program Conference, MIT (2009) [ PDF ]
  • Decentralized Detection Schemes Applied to Cognitive Radio Networks - Vodafone Symposium (2007) [ PDF ]

Patents

  • Efficient MCMC Sampling with Implicit Shape Representations. U.S. Patent #8744185 B2, June 3, 2014.


*Note: Movies only play in powerpoint and are usually compressed using the Xvid (*.avi) or the H264 (*.mp4) codec.