ECCV 2014 - Zurich

13th European Conference on Computer Vision (ECCV), Zurich 2014

The European Conference on Computer Vision is one of the top conferences for researchers in this field and is held biennially in alternation with the International Conference on Computer Vision. It was first held in 1990 in Antibes (France) with subsequent conferences in Santa Margherita Ligure (Italy) in 1992, Stockholm (Sweden) in 1994, Cambridge (UK) in 1996, Freiburg (Germany) in 1998, Dublin (Ireland) in 2000, Copenhagen (Denmark) in 2002, Prague (Czech Republic) in 2004, Graz (Austria) in 2006, Marseille (France) in 2008, and Heraklion (Greece) in 2010 and 2012 in Florence (Italy). To our great delight, the 13th conference is held in Zürich, Switzerland.

ECCV has an established tradition of very high scientific quality and an overall duration of one week.

For more information please visit the ECCV 2014 website.

Categories

Top » Computer Science » Computer Vision


Opening & Awards
[syn] 3178 views, 20:18 Opening & AwardsOpening & Awards Tomas Pajdla, Marc Pollefeys Tomas Pajdla, Marc Pollefeys
Tracking and Activity Recognition
[syn] 3607 views, 18:01 Visual Tracking by Sampling Tree-Structured Graphical ModelsVisual Tracking by Sampling Tree-Structured Graphical Models Seunghoon Hong Seunghoon Hong	[syn] 5749 views, 17:26 Tracking Interacting Objects Optimally Using Integer ProgrammingTracking Interacting Objects Optimally Using Integer Programming Xinchao Wang Xinchao Wang	[syn] 3121 views, 16:54 Learning Latent Constituents for Recognition of Group Activities in VideoLearning Latent Constituents for Recognition of Group Activities in Video Borislav Antic Borislav Antic
Recognition
[syn] 8996 views, 18:05 Large-Scale Object Classification using Label Relation GraphsLarge-Scale Object Classification using Label Relation Graphs Jia Deng Jia Deng	[syn] 8202 views, 18:33 30Hz Object Detection with DPM V530Hz Object Detection with DPM V5 David Forsyth David Forsyth	[syn] 3593 views, 15:37 Knowing a Good HOG Filter When You See It: Efficient Selection of Filters for DetectionKnowing a Good HOG Filter When You See It: Efficient ... Ejaz Ahmed Ejaz Ahmed	[syn] 3485 views, 16:06 Linking People in Videos with “Their” Names Using Coreference ResolutionLinking People in Videos with “Their” Names Using Coreference Resolution Vignesh Ramanathan Vignesh Ramanathan
Learning and Inference
[syn] 16412 views, 17:05 Visualizing and Understanding Convolutional NetworksVisualizing and Understanding Convolutional Networks Matthew Zeiler Matthew Zeiler 1 comment	[syn] 6247 views, 16:57 Part-Based R-CNNs for Fine-Grained Category DetectionPart-Based R-CNNs for Fine-Grained Category Detection Ning Zhang Ning Zhang	[syn] 4375 views, 14:24 Coarse-to-Fine Auto-encoder Networks (CFAN) for Real-time Face AlignmentCoarse-to-Fine Auto-encoder Networks (CFAN) for Real-time Face Alignment Jie Zhang Jie Zhang	[syn] 2765 views, 17:46 From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD MatricesFrom Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices Mathieu Salzmann Mathieu Salzmann	[syn] 4358 views, 16:26 Pose Machines: Articulated Pose Estimation via Inference MachinesPose Machines: Articulated Pose Estimation via Inference Machines Varun Ramakrishna Varun Ramakrishna
Structure from Motion and Feature Matching
[syn] 2631 views, 14:08 Progressive Mode-Seeking on Graphs for Sparse Feature MatchingProgressive Mode-Seeking on Graphs for Sparse Feature Matching Lingqiao Liu Lingqiao Liu	[syn] 2366 views, 17:10 Globally Optimal Inlier Set Maximization With Unknown Rotation and Focal LengthGlobally Optimal Inlier Set Maximization With Unknown Rotation and Focal ... Jean-Charles Bazin Jean-Charles Bazin	[syn] 2679 views, 17:03 Match Selection and Refinement for Highly Accurate Two-View Structure from MotionMatch Selection and Refinement for Highly Accurate Two-View Structure from ... Zhe Liu Zhe Liu	[syn] 14499 views, 18:07 LSD-SLAM: Large-Scale Direct Monocular SLAMLSD-SLAM: Large-Scale Direct Monocular SLAM Jakob Engel Jakob Engel 5 comments
Computational Photography and Low-Level Vision
[syn] 2920 views, 17:34 Depth-of-Field and Coded Aperture Imaging on XSlit LensDepth-of-Field and Coded Aperture Imaging on XSlit Lens Jingyi Yu Jingyi Yu	[syn] 4073 views, 17:53 Refraction Wiggles for Measuring Fluid Depth and Velocity from VideoRefraction Wiggles for Measuring Fluid Depth and Velocity from Video Tianfan Xue Tianfan Xue	[syn] 4437 views, 15:00 Blind Deblurring Using Internal Patch RecurrenceBlind Deblurring Using Internal Patch Recurrence Tomer Michaeli Tomer Michaeli	[syn] 2401 views, 18:44 Crisp Boundary Detection Using Pointwise Mutual InformationCrisp Boundary Detection Using Pointwise Mutual Information Phillip Isola Phillip Isola	[syn] 4069 views, 17:15 Rolling Guidance FilterRolling Guidance Filter Jiaya Jia Jiaya Jia
Vision
[syn] 3202 views, 18:00 Bayesian Nonparametric Intrinsic Image DecompositionBayesian Nonparametric Intrinsic Image Decomposition Jason Chang Jason Chang	[syn] 15672 views, 18:46 Face detection without bells and whistlesFace detection without bells and whistles Markus Mathias Markus Mathias	[syn] 2404 views, 18:06 On Image Contours of Projective ShapesOn Image Contours of Projective Shapes Jean Ponce Jean Ponce	[syn] 2348 views, 18:22 Programmable Automotive HeadlightsProgrammable Automotive Headlights Robert Tamburo Robert Tamburo
Segmentation and Saliency
[syn] 3053 views, 17:58 Convexity Shape Prior for SegmentationConvexity Shape Prior for Segmentation Lena Gorelick Lena Gorelick	[syn] 2529 views, 14:27 Pseudo-Bound Optimization for Binary EnergiesPseudo-Bound Optimization for Binary Energies Meng Tang Meng Tang	[syn] 2864 views, 17:35 A Closer Look at Context: From Coxels to the Contextual Emergence of Object SaliencyA Closer Look at Context: From Coxels to the Contextual ... Rotem Mairon Rotem Mairon	[syn] 3365 views, 15:12 Geodesic Object ProposalsGeodesic Object Proposals Philipp Krähenbühl Philipp Krähenbühl	[syn] 2846 views, 15:07 Microsoft COCO: Common Objects in ContextMicrosoft COCO: Common Objects in Context Tsung-Yi Lin Tsung-Yi Lin
Context and 3D Scenes
[syn] 15086 views, 18:20 Sliding Shapes for 3D Object Detection in Depth ImagesSliding Shapes for 3D Object Detection in Depth Images Shuran Song Shuran Song	[syn] 5385 views, 15:17 Integrating Context and Occlusion for Car Detection by Hierarchical And-Or ModelIntegrating Context and Occlusion for Car Detection by Hierarchical And-Or ... Bo Li Bo Li	[syn] 8011 views, 19:05 PanoContext: A Whole-room 3D Context Model for Panoramic Scene UnderstandingPanoContext: A Whole-room 3D Context Model for Panoramic Scene Understanding Yinda Zhang Yinda Zhang	[syn] 3444 views, 16:12 Unfolding an Indoor Origami WorldUnfolding an Indoor Origami World David Ford Fouhey David Ford Fouhey
Motion and 3D Scene Analysis
[syn] 2441 views, 17:02 Dense Semi-rigid Scene Flow Estimation from RGBD ImagesDense Semi-rigid Scene Flow Estimation from RGBD Images Julián Quiroga Julián Quiroga	[syn] 3015 views, 14:00 Video Pop-up: Monocular 3D Reconstruction of Dynamic ScenesVideo Pop-up: Monocular 3D Reconstruction of Dynamic Scenes Chris Russell Chris Russell	[syn] 2213 views, 17:50 Joint Object Class Sequencing and Trajectory Triangulation (JOST)Joint Object Class Sequencing and Trajectory Triangulation (JOST) Enliang Zheng Enliang Zheng	[syn] 2401 views, 17:09 Scene ChronologyScene Chronology Kevin Matzen Kevin Matzen
Closing
[syn] 2287 views, 01:37 ClosingClosing Marc Pollefeys, Luc Van Gool Marc Pollefeys, Luc Van Gool	[syn] 5174 views, 02:56 ECCV 2016 announcementECCV 2016 announcement Theo Gevers Theo Gevers

Write your own review or comment:

Comment:
Name:
Email address:
URL:

make sure you have javascript enabled or clear this field:

View order

Topic taxonomy

Type of content

Language

Year

ECCV 2014 - Zurich

13th European Conference on Computer Vision (ECCV), Zurich 2014

Opening & Awards

Tracking and Activity Recognition

Recognition

Learning and Inference

Structure from Motion and Feature Matching

Computational Photography and Low-Level Vision

Vision

Segmentation and Saliency

Context and 3D Scenes

Motion and 3D Scene Analysis

Closing

Write your own review or comment:

From:
To: