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We talked about Filters Edges Corners Interest Points Descriptors Image Stitching Stereo SFM
We talked about Reconstruction Flow Face Detection Face Recognition Pedestrian Detection Part-Based Recognition …
What else? Several other interesting stuff that we wanted to talk about them
Person Sky Tree Car Image Segmentation
drive- way sky house ? grass Context-aware visual discovery grass sky truck house ? drive- way grass sky house drive- way fence ? ? ?? 10 Context in supervised recognition: [Torralba 2003], [Hoiem et al. 2006], [He et al. 2004], [Shotton et al. 2006], [Heitz & Koller 2008], [Rabinovich et al. 2007], [Galleguillos et al. 2008], [Tu 2008], [Parikh et al. 2008], [Gould et al. 2009], [Malisiewicz & Efros 2009], [Lazebnik 2009]
Attributes Vehicle Wheel Animal Leg Head Four-legged Mammal Can run Can Jump Is Herbivorous Facing right Moves on road Facing right
Context Neelima Chavali ECE /21/2013. Roadmap Introduction Paper1 – Motivation – Problem statement – Approach – Experiments & Results Paper 2 Experiments.
Object-Graphs for Context-Aware Category Discovery Yong Jae Lee and Kristen Grauman University of Texas at Austin 1.
Challenges to image parsing researchers Lana Lazebnik UNC Chapel Hill sky sidewalk building road car person car mountain.
Extracting Simple Verb Frames from Images Toward Holistic Scene Understanding Prof. Daphne Koller Research Group Stanford University Geremy Heitz DARPA.
LARGE-SCALE NONPARAMETRIC IMAGE PARSING Joseph Tighe and Svetlana Lazebnik University of North Carolina at Chapel Hill CVPR 2011Workshop on Large-Scale.
12/7/10 Looking Back, Moving Forward Computational Photography Derek Hoiem, University of Illinois Photo Credit Lee Cullivan.
Dynamic 3D Scene Analysis from a Moving Vehicle Young Ki Baik (CV Lab.) (Wed)
Detection Evolution with Multi- Order Contextual Co-Occurrence Guang Chen (Missouri) Yuanyuan Ding (Epson) Jing Xiao (Epson) Tony Han (Missouri)
LARGE-SCALE IMAGE PARSING Joseph Tighe and Svetlana Lazebnik University of North Carolina at Chapel Hill road building car sky.
Describing Images Using Attributes. Describing Images Farhadi et.al. CVPR 2009.
Visual Scene Understanding (CS 598) Derek Hoiem Course Number: Instructor: Derek Hoiem Room: Siebel Center 1109 Class Time: Tuesday and Thursday.
Learning Spatial Context: Using stuff to find things Geremy Heitz Daphne Koller Stanford University October 13, 2008 ECCV 2008.
A SAMPLE RECOGNITION PROBLEM Joseph Tighe University of North Carolina at Chapel Hill.
Visual Object Recognition Rob Fergus Courant Institute, New York University
Grouplet: A Structured Image Representation for Recognizing Human and Object Interactions Bangpeng Yao and Li Fei-Fei Computer Science Department, Stanford.
Learning Shared Body Plans Ian Endres University of Illinois work with Derek Hoiem, Vivek Srikumar and Ming-Wei Chang.
Graph Cut based Inference with Co-occurrence Statistics Ľubor Ladický, Chris Russell, Pushmeet Kohli, Philip Torr.
Representation in Vision Derek Hoiem CS 598, Spring 2009 Jan 22, 2009.
Training Regimes Motivation Allow state-of-the-art subcomponents With “Black-box” functionality This idea also occurs in other application areas.
An opposition to Window- Scanning Approaches in Computer Vision Presented by Tomasz Malisiewicz March 6, 2006 Advanced The Robotics Institute.
Modeling Mutual Context of Object and Human Pose in Human-Object Interaction Activities Bangpeng Yao and Li Fei-Fei Computer Science Department, Stanford.
Li Fei-Fei, UIUC Rob Fergus, MIT Antonio Torralba, MIT Recognizing and Learning Object Categories ICCV 2005 Beijing, Short Course, Oct 15.
Beyond bags of features: Adding spatial information Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba.
Agenda Introduction Bag-of-words model Visual words with spatial location Part-based models Discriminative methods Segmentation and recognition Recognition-based.
TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation J. Shotton ; University of Cambridge J. Jinn,
Learning and Inference in Vision: from Features to Scene Understanding Jonathan Huang, Tomasz Malisiewicz MLD Student Research Symposium, 2009.
The University of Texas at Austin Vision-Based Pedestrian Detection for Driving Assistance Marco Perez.
1 Integrating Vision Models for Holistic Scene Understanding Geremy Heitz CS223B March 4 th, 2009.
Are Categories Necessary in a Data-Rich World? Alexei (Alyosha) Efros CMU Joint work with Tomasz Malisiewicz.
Saliency & attention (P) Lavanya Sharan April 4th, 2011.
Putting Context into Vision Derek Hoiem September 15, 2004.
Computer Vision (CSE P576) Staff Prof: Steve Seitz TA: Jiun-Hung Chen Web Page
Recovering Surface Layout from a Single Image D. Hoiem, A.A. Efros, M. Hebert Robotics Institute, CMU Presenter: Derek Hoiem CS 598, Spring 2009 Jan 29,
9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele.
Real-time On-Road Vehicle Detection With Optical Flows and Haar-like feature detector Department of Computer Science, University of Illinois Urbana-Champaign.
What, Where & How Many? Combining Object Detectors and CRFs Ľubor Ladický, Paul Sturgess, Karteek Alahari, Christopher Russell, Philip H.S. Torr
Joint Optimisation for Object Class Segmentation and Dense Stereo Reconstruction Ľubor Ladický, Paul Sturgess, Christopher Russell, Sunando Sengupta, Yalin.
Computer Vision (CSE P 576) Instructor: Larry Zitnick TA: Dun-Yu Hsiao
Learning Spatial Context: Can stuff help us find things? Geremy Heitz Daphne Koller April 14, 2008 DAGS Stuff (n): Material defined by a homogeneous or.
Unsupervised discovery of visual object class hierarchies Josef Sivic (INRIA / ENS), Bryan Russell (MIT), Andrew Zisserman (Oxford), Alyosha Efros (CMU)
Visual Odometry for Ground Vehicle Applications David Nistér, Oleg Naroditsky, and James Bergen Sarnoff Corporation CN5300 Princeton, New Jersey
A Brief Overview of Computer Vision Jinxiang Chai.
VIVID Project Attacking plan. Problems Description What we have? – Map(? ) – Satellite Imagery – Aerial Video and Mosaic Images Target – Road, building,
Leo Zhu CSAIL MIT Joint work with Chen, Yuille, Freeman and Torralba 1.
Computer Vision Why study Computer Vision? Images and movies are everywhere Fast-growing collection of useful applications –building representations.
Scene Understanding through Transfer Learning Stephen Gould Ben Packer Geremy Heitz Daphne Koller DARPA Update September 11, 2008.
1 Outline Overview Integrating Vision Models CCM: Cascaded Classification Models Learning Spatial Context TAS: Things and Stuff Descriptive Querying of.
Object Recognition: History and Overview Slides adapted from Fei-Fei Li, Rob Fergus, Antonio Torralba, and Jean Ponce.
Computational Photography CS498dh Derek Hoiem 8/25/11.
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