Xiaofeng Ren

Intel Labs Seattle
1100 NE 45th St., 6th Floor
Seattle, WA 98105

Phone:
Fax:
Email:
(206) 545-2523
(206) 633-6504
xiaofeng.ren at intel.com


Note: you will be forwarded to my new professional webpage hosted at UW.
I am a research scientist at Intel Labs Seattle. I am currently part of the RGB-D research team that is working on solving computer vision in everyday life settings and applications using RGB-D (color+depth, a.k.a. Kinect style) cameras, ranging from 3D mapping and modeling to everyday object recognition. (Current CV).

I am interested in all aspects of computer vision, as I believe all are needed to solve it. I have worked on many vision problems including local descriptors, boundary detection, image segmentation, figure-ground grouping, object and pose recognition, human body detection and pose estimation, object segmentation and tracking, optical flow, and 3D reconstruction. Recently, I have had opportunities to work on vision-related problems in robotics and human-computer interaction.

I joined Intel Labs Seattle in 2008. Prior to 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


Publications


Research Projects

For most recent projects, please visit my official page at Intel Labs Seattle.

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