Paper / Citation Abstract We propose to learn pixel-level segmentations of objects from weakly labeled (tagged) internet videos. Espe cially, given a large collection of raw YouTube content, along with potentially noisy tags, our goal is to automatically generate spatiotemporal masks for each object, such as a “dog”, without employing any pre-trained object detectors. We formulate […]
Video Enhancement and Analysis: From Content Analysis to Video Stabilization for YouTube Irfan Essa Georgia Institute of Technology October 14, 2012 15:00 – 16:00, Huxely Room. South Kensington Campus, Imperial College, London Abstract The talk will describe a variety of efforts undertaken on analysis of video to enhancement and synthesis of video. An overview of […]
Video Stabilization and Rolling Shutter Removal on YouTube Abstract In this talk, I will over a variety of approaches my group is working on for video analysis and enhancement. In particular, I will describe our approach for a video stabilizer (currently implemented on YouTube) and its extensions. This work is in collaboration with Matthias Grundmann […]
At IEEE CVPR 2012 is in Providence RI, from Jun 16 – 21, 2012. Busy week ahead meeting good friends and colleagues. Here are some highlights of what my group is involved with. Paper in Main Conference K. Kim, D. Lee, and I. Essa (2012), “Detecting Regions of Interest in Dynamic Scenes with Camera Motions,” in […]
Detecting Regions of Interest in Dynamic Scenes with Camera Motions Abstract We present a method to detect the regions of interests in moving camera views of dynamic scenes with multiple mov- ing objects. We start by extracting a global motion tendency that reflects the scene context by tracking movements of objects in the scene. We […]
Extracting Content and Context from Video. (Presentation at CVPR 2012 workshop on Large Scale Video Search and Mining 2012, June 21, 2012)Irfan EssaGEORGIA Tech Abstract In this talk, I will describe various efforts aimed at extracting context and content from video. I will highlight some of our recent work in extracting spatio-temporal features and the […]
Videos Understanding: Extracting Content and Context from Video. (Presentation at the International Workshop on Computer Vision 2012, Ortigia, Siracusa, Sicily, May 22-24, 2012.) Irfan Essa GEORGIA Tech Abstract In this talk, I will describe various efforts aimed at extracting context and content from video. I will highlight some of our recent work in extracting spatio-temporal features […]
Here is an excerpt from a Google Research Blog on our Video Stabilization on YouTube. Now even more improved. One thing we have been working on within Research at Google is developing methods for making casual videos look more professional, thereby providing users with a better viewing experience. Professional videos have several characteristics that differentiate […]
Abstract We present a novel algorithm for efficiently removing rolling shutter distortions in uncalibrated streaming videos. Our proposed method is calibration-free as it does not need any knowledge of the camera used, nor does it require calibration using specially recorded calibration sequences. Our algorithm can perform rolling shutter removal under varying focal lengths, as in […]
Our following paper was just awarded the Excellent Paper for 2011 in Computer Vision by Google Research. Casually shot videos captured by handheld or mobile cameras suffer from significant amount of shake. Existing in-camera stabilization methods dampen high-frequency jitter but do not suppress low-frequency movements and bounces, such as those observed in videos captured by […]