Xiaofeng Ren

Amazon - Otter (SEA55)
2301 5th Ave., Seattle, WA 98121

xiaofenr at amazon dot com

As of March 2013, I joined Amazon as a principal research scientist for a shiny new project that centers around computer vision. It's about time that computer vision makes a real impact in the real world on real people's lives :-) And we will try our best to make it happen at Amazon. I still have an affiliate position at University of Washington. (Current CV,Google Scholar).

I am interested in all aspects of computer vision, as I believe all are needed to solve it. Most recently I worked on using RGB-D (color+depth, a.k.a. Kinect style) cameras, ranging from 3D mapping and modeling to everyday object recognition. I worked on many other vision problems, including image descriptors, boundary detection, image segmentation, figure-ground grouping, object and pose recognition, human body detection and pose estimation, object segmentation and tracking, and optical flow. I had opportunities to work on vision-related problems in robotics and human-computer interaction.

I was a research scientist at Intel Labs during 2008-2013, working closely with faculty and students at University of Washington. Prior to sunny Seattle, I was a research assistant professor at the Toyota Technological Institute at Chicago (TTI-C). I received my Ph.D. from U.C. Berkeley in 2006, under the supervision of Jitendra Malik.

Recent Updates


(Old) Research Projects

Discriminative Viewpoint Classification
RGB-D Mapping
Egocentric Object Recognition
Multi-Scale Improves Boundary Detection
Local Grouping for Optical Flow
Finding and Tracking People in Archive Films
Tracking as Repeated Figure/Ground Segmentation
Line-based Aspect Learning and Matching
Figure-ground organization in natural images
Cue Integration in Figure/Ground Labeling
Scale-Invariant Contour Completion using Conditional Random Fields
Using Shapemes for Mid-level Vision
A Scale-Invariant Image Representation: the CDT Graph
Pairwise Constraints between Human Body Parts
Learning Discriminative Models for Image Segmentation
Human Body Configuration from Bottom-Up: a Segmentation-based Approach
Contours in Natural Images and Scale Invariance
Superpixel: Empirical Studies and Applications