Citation Abstract We present an algorithm for finding temporally consistent occlusion boundaries in videos to support the segmentation of dynamic scenes. We learn occlusion boundaries in a pairwise Markov random field (MRF) framework. We first estimate the probability of a spatiotemporal edge being an occlusion boundary by using appearance, flow, and geometric features. Next, we […]
Four papers accepted at the IEEE Winter Conference on Applications of Computer Vision (WACV) 2015. See you at Waikoloa Beach, Hawaii! Last one was also the WINNER of Best Paper Award (see http://wacv2015.org/). More details coming soon.
Today, the Inaugural Offering of the Computational Photography (CS 6475) was launched for the Georgia Tech’s Online MSCS Program using the Udacity platform. Course Description CS 6475* (3-0-3): Computational Photography – (Instructor: Irfan Essa) – This class explores how computation impacts the entire workflow of photography, which is traditionally aimed at capturing light from a (3D) scene to […]
William Mong Distinguished Lecture at the University of Hong Kong on "Video Cameras are Everywhere: Data-Driven Methods for Video Analysis and Enhancement"
Video Cameras are Everywhere: Data-Driven Methods for Video Analysis and Enhancement Irfan Essa (prof.irfanessa.com) Georgia Institute of Technology School of Interactive Computing GVU and RIM @ GT Centers Abstract In this talk, I will start with describing the pervasiveness of image and video content, and how such content is growing with the ubiquity of cameras. […]
Paper in BMCV (2014): "Depth Extraction from Videos Using Geometric Context and Occlusion Boundaries"
We present an algorithm to estimate depth in dynamic video scenes.We present an algorithm to estimate depth in dynamic video scenes. We propose to learn and infer depth in videos from appearance, motion, occlusion boundaries, and geometric context of the scene. Using our method, depth can be estimated from unconstrained videos with no requirement of […]
Title : Temporally Consistent Semantic Segmentation in Videos S. Hussain Raza, Ph. D. Candidate in ECE (https://sites.google.com/site/shussainraza5/) Committee: Prof. Irfan Essa (advisor), School of Interactive Computing Prof. David Anderson (co-advisor), School of Electrical and Computer Engineering Prof. Frank Dellaert, School of Interactive Computing Prof. Anthony Yezzi, School of Electrical and Computer Engineering Prof. Chris Barnes, […]
Today, two of my Ph. D. Students defended their Dissertations. Back to back. Congrats to both as they are both done. Thesis title: Surgical Skill Assessment Using Motion Texture analysis Student: Yachna Sharma, Ph. D. Candidate in ECE http://users.ece.gatech.edu/~ysharma3/ Date/Time : 2nd April, 1:00 pm Title : Temporally Consistent Semantic Segmentation in Videos S. Hussain […]
Paper in CVIU 2013 "A Visualization Framework for Team Sports Captured using Multiple Static Cameras"
Abstract We present a novel approach for robust localization of multiple people observed using a set of static cameras. We use this location information to generate a visualization of the virtual offside line in soccer games. To compute the position of the offside line, we need to localize players′ positions, and identify their team roles. […]
Paper in ACM Ubicomp 2013 "Technological approaches for addressing privacy concerns when recognizing eating behaviors with wearable cameras"
Abstract First-person point-of-view (FPPOV) images taken by wearable cameras can be used to better understand people’s eating habits. Human computation is a way to provide effective analysis of FPPOV images in cases where algorithmic approaches currently fail. However, privacy is a serious concern. We provide a framework, the privacy-saliency matrix, for understanding the balance between […]
Teaching at the ICVSS 2013, in Calabria, Italy, July 2013 (Programme) Computational Video: Post-processing Methods for Stabilization, Retargeting and Segmentation Irfan Essa (This work in collaboration with Matthias Grundmann, Daniel Castro, Vivek Kwatra, Mei Han, S. Hussian Raza). Abstract We address a variety of challenges for analysis and enhancement of Computational Video. We present novel […]