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Google at CVPR 2011
Thursday, June 16, 2011
Posted by Mei Han and Sergey Ioffe, Research Team
The computer vision community will get together in Colorado Springs the week of June 20th for the
IEEE International Conference on Computer Vision and Pattern Recognition
(CVPR 2011). This year will see a record number of people attending the conference and 27 co-located workshops and tutorials. The registration was closed at 1500 attendees even before the conference started.
Computer Vision is at the core of many Google products, such as
Image Search
,
YouTube
,
Street View
,
Picasa
, and
Goggles
, and as always, Google is involved in several ways with CVPR.
Andrew Senior
is serving as an area chair of CVPR 2011 and many Googlers are reviewers. Googlers also co-authored these papers:
Where's Waldo: Matching People in Images of Crowds
by Rahul Garg, Deva Ramanan, Steve Seitz, Noah Snavely
Visual and Semantic Similarity in ImageNet
by Thomas Deselaers, Vittorio Ferrari
Multicore Bundle Adjustment
by Changchang Wu, Sameer Agarwal, Brian Curless, Steve Seitz
A Hierarchical Conditional Random Field Model for Labeling and Segmenting Images of Street Scenes
by Qixing Huang, Mei Han, Bo Wu, Sergey Ioffe
Kernelized Structural SVM Learning for Supervised Object Segmentation
by Luca Bertelli, Tianli Yu, Diem Vu, Salih Gokturk
Discriminative Tag Learning on YouTube Videos with Latent Sub-tags
by Weilong Yang, George Toderici
Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths
by Matthias Grundmann, Vivek Kwatra, Irfan Essa
Image Saliency: From Local to Global Context
by Meng Wang, Janusz Konrad, Prakash Ishwar, Yushi Jing, Henry Rowley
If you are attending the conference, stop by Google’s exhibition booth. In addition to talking with Google researchers, you will get to see examples of exciting computer vision research that has made it into Google products including, among others, the following:
Google Earth Facade Shadow Removal
by Mei Han, Vivek Kwatra, and Shengyang Dai
We will demonstrate our technique for removing shadows and other lighting/texture artifacts from building facades in Google Earth. We obtain cleaner, clearer, and more uniform textures which provide users with an improved visual experience.
Video Stabilization on YouTube Editor
by Matthias Grundmann, Vivek Kwatra, and Irfan Essa
Casually shot videos captured by handheld or mobile cameras suffer from significant amount of shake. In contrast, professionally shot video usually employs stabilization equipment such as tripods or camera dollies, and employ ease-in and ease-out for transitions. Our technique mimics these cinematographic principles, by optimally dividing the original, shaky camera path into a set of segments and approximating each with either constant, linear or parabolic motion using a computationally efficient and stable algorithm. We will showcase a live version of our algorithm, featuring real-time performance and interactive control, which is publicly available at youtube.com/editor.
Tag Suggest for YouTube
by George Toderici and Mehmet Emre Sargin
YouTube offers millions of users the opportunity to upload videos and share them with their friends. Many users would love to have their videos discoverable but don't annotate them properly. One new feature on YouTube that seeks to address this problem is tag prediction based on video content and independently based on text metadata.
6/17/2011 UPDATE: "Posted by" was changed to include Sergey Ioffe.
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