Jon Barron

I recently finished my PhD in EECS at UC Berkeley, where I was advised by Jitendra Malik and funded by the NSF GRFP. I am now at Google[x], in Marc Levoy's group, working on computational photography.

I've spent time at MIT CSAIL, Captricity, NASA Ames Research Center, Google NYC, the NYU Media Research Lab, and the Novartis Institutes for BioMedical Research. I've worked on Astrometry.net. I did my bachelors at the University of Toronto.

Email / CV / Biography / Thesis / Facebook / Google+ / LinkedIn

Research

I'm interested in computer vision, machine learning, and computational photography. Most of my research is about inferring the physical world (shape, paint, light, etc) from a single image. I also work in astronomy and biological image analysis.

Estee

Shape, Illumination, and Reflectance from Shading
Jonathan T. Barron, Jitendra Malik
Tech Report, 2013
supplemental / bibtex / keynote (or powerpoint, PDF) / movie / code & data

We present SIRFS, which can estimate shape, chromatic illumination, reflectance, and shading from a single image of an masked object.
This paper subsumes our CVPR 2011, CVPR 2012, and ECCV 2012 papers.

ArbalaezCVPR2014

Multiscale Combinatorial Grouping
Pablo Arbeláez, Jordi Pont-Tuset, Jonathan T. Barron, Ferran Marqués,
Jitendra Malik
Computer Vision and Pattern Recognition (CVPR), 2014
project page / bibtex / fast eigenvector code

We produce state-of-the-art contours, regions and object candidates, and we compute normalized-cuts eigenvectors 20× faster.

ICCV2013_anim

Volumetric Semantic Segmentation using Pyramid Context Features
Jonathan T. Barron, Pablo Arbeláez, Soile V. E. Keränen, Mark D. Biggin,
David W. Knowles, Jitendra Malik
International Conference on Computer Vision (ICCV), 2013
supplemental / poster / bibtex / movie 1 (or mp4) / movie 2 (or mp4)

We present a technique for extremely efficient per-voxel linear classification, which enables extremely accurate semantic segmentation of volumetric Drosophila imagery.

3DSP

3D Self-Portraits
Hao Li, Etienne Vouga, Anton Gudym, Linjie Luo, Jonathan T. Barron, Gleb Gusev
SIGGRAPH Asia, 2013
movie / shapify.me / bibtex

Our system allows users to create textured 3D models of themselves in arbitrary poses using only a single 3D sensor.

CVPR2013_anim

Intrinsic Scene Properties from a Single RGB-D Image
Jonathan T. Barron, Jitendra Malik
Computer Vision and Pattern Recognition (CVPR), 2013   (Oral Presentation)
supplemental / bibtex / talk / keynote (or powerpoint, PDF) / code & data

By embedding mixtures of shapes & lights into a soft segmentation of an image, and by leveraging the output of the Kinect, we can extend SIRFS to scenes.

Boundary_png

Boundary Cues for 3D Object Shape Recovery
Kevin Karsch, Zicheng Liao, Jason Rock, Jonathan T. Barron, Derek Hoiem
Computer Vision and Pattern Recognition (CVPR), 2013
supplemental / bibtex

Boundary cues (like occlusions and folds) can be used for shape reconstruction, which improves object recognition for humans and computers.

ECCV2012_anim

Color Constancy, Intrinsic Images, and Shape Estimation
Jonathan T. Barron, Jitendra Malik
European Conference on Computer Vision (ECCV), 2012
supplemental / bibtex / poster / movie

This paper is subsumed by SIRFS.

Big J

Shape, Albedo, and Illumination from a Single Image of an Unknown Object
Jonathan T. Barron, Jitendra Malik
Computer Vision and Pattern Recognition (CVPR), 2012
supplemental / bibtex / poster

This paper is subsumed by SIRFS.

b3do

A Category-Level 3-D Object Dataset: Putting the Kinect to Work
Allison Janoch, Sergey Karayev, Yangqing Jia, Jonathan T. Barron, Mario Fritz, Kate Saenko, Trevor Darrell
International Conference on Computer Vision (ICCV) 3DRR Workshop, 2011
bibtex / "smoothing" code

We present a large RGB-D dataset of indoor scenes and investigate ways to improve object detection using depth information.

safs_small

High-Frequency Shape and Albedo from Shading using Natural Image Statistics
Jonathan T. Barron, Jitendra Malik
Computer Vision and Pattern Recognition (CVPR), 2011
bibtex

This paper is subsumed by SIRFS.

fast-texture

Discovering Efficiency in Coarse-To-Fine Texture Classification
Jonathan T. Barron, Jitendra Malik
Technical Report, 2010
bibtex

We introduce a model and feature representation for joint texture classification and segmentation that learns how to classify accurately and when to classify efficiently. This allows for sub-linear coarse-to-fine classification.

blind-date

Blind Date: Using Proper Motions to Determine the Ages of Historical Images
Jonathan T. Barron, David W. Hogg, Dustin Lang, Sam Roweis
The Astronomical Journal, 136, 2008

Using known catalog proper motions, we can accurately estimate the date of origin of historical imagery given only raw pixel data.

clean-usnob

Cleaning the USNO-B Catalog Through Automatic Detection of Optical Artifacts
Jonathan T. Barron, Christopher Stumm, David W. Hogg, Dustin Lang, Sam Roweis
The Astronomical Journal, 135, 2008

We use computer vision techniques to identify and remove diffraction spikes and reflection halos in the USNO-B Catalog.

In use at Astrometry.net

Course Projects
prl

Parallelizing Reinforcement Learning
Jonathan T. Barron, Dave Golland, Nicholas J. Hay, 2009


Markov Decision Problems which lie in a low-dimensional latent space can be decomposed, allowing modified RL algorithms to run orders of magnitude faster in parallel.

Teaching
pacman

CS188 - Fall 2010 (GSI)

CS188 - Spring 2011 (GSI)


people seem to like my website