Jon Barron

I am a senior research scientist at Google, currently working on computer vision and computational photography.

I recently finished my PhD in EECS at UC Berkeley, where I was advised by Jitendra Malik and funded by the NSF GRFP. I've spent time at MIT CSAIL, Captricity, NASA Ames, Google NYC, the NYU MRL, and Novartis. I worked on I did my bachelors at the University of Toronto.

Email / CV / Biography / Thesis / Google Scholar / LinkedIn


I'm interested in computer vision, machine learning, statistics, optimization, image processing, and computational photography. Much of my research is about inferring the physical world (shape, paint, light, etc) from images. I have also worked in astronomy and biology.


Fast Bilateral-Space Stereo for Synthetic Defocus
Jonathan T. Barron, Andrew Adams, YiChang Shih, Carlos Hernández
Computer Vision and Pattern Recognition (CVPR), 2015
supplemental / bibtex

By embedding a stereo optimization problem into "bilateral-space" we can very quickly solve for an edge-aware depth map, letting us render beautiful depth-of-field effects.

This technology is used by the Google Camera "Lens Blur" feature.


Shape, Illumination, and Reflectance from Shading
Jonathan T. Barron, Jitendra Malik
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015
supplemental / bibtex / keynote (or powerpoint, PDF) / movie / code & data / rant

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.


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.


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 efficient per-voxel linear classification, which enables accurate and fast semantic segmentation of volumetric Drosophila imagery.


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

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


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 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.


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.


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.


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

This paper is subsumed by SIRFS.


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

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: 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 the relative motions of stars we can accurately estimate the date of origin of historical astronomical images.


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

Course Projects

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.


CS188 - Fall 2010 (GSI)

CS188 - Spring 2011 (GSI)

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