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VOCVALC 2018 : 2nd International workshop on Visual Odometry and Computer Vision Applications based Location Clues, in conjunction with CVPR 2018


When Jun 22, 2018 - Jun 22, 2018
Where Salt Lake City, USA
Submission Deadline Apr 2, 2018
Notification Due Apr 15, 2018
Final Version Due Apr 20, 2018
Categories    computer vision   robotics   artificial intelligence   image processing

Call For Papers

Call for papers

With the advent of autonomous driving and augmented reality, the applications of visual odometry are significantly growing. The development of smart-phones and cameras is also making the visual odometry more accessible to common users in daily life. With the increasing efforts devoted to accurately computing the position information, emerging applications based on location context, such as scene understanding, city navigation and tourist recommendation, have gained significant growth. The location information can bring a rich context to facilitate a large number of challenging problems, such as landmark and traffic sign recognition under various weather and light conditions, and computer vision applications on entertainment based on location information, such as Pokemon. The motivation for the proposed workshop is soliciting scalable algorithms and systems for addressing the ever increasing demand of accurate and real-time visual odometry, as well as the methods and applications based on the location clues. This workshop invites papers in the areas including advances in visual odometry and its applications related to computer vision in topics listed below, but not limited:

Image-based localization and navigation
Monocular and stereo visual odometry
Visual odometry applications on autonomous driving
Augmented reality based on visual odometry
Robust pose estimation solutions
Multi-model visual sensor data fusion
Real-time object tracking
3D scene modeling
Application of deep learning on visual odometry
Large-scale SLAM
Map generation
Scene understanding and semantic labeling
Rendering and visualization of large-scale models
Feature representation, indexing, storage and analysis
Feature extraction and matching
Object detection and recognition based on location context
Landmark mining and tourism recommendation
Video surveillance
Benchmark datasets collection

Organizers/Program chairs:

Guoyu Lu, Rochester Institute of Technology
Friedrich Fraundorfer, Graz University of Technology
Yan Yan, Texas State University
Nicu Sebe, University of Trento
Chandra Kambhamettu, University of Delaware

Program committee:

Rudolf Mester, Linköping University
Adrien Bartoli, University of Auvergne
Riad Hammoud, BAE Systems
Carl Salvaggio, Rochester Institute of Technology
Will Maddern, Oxford University
Cornelia Fermuller, University of Maryland
Vincent Lepetit, Graz University of Technology
Jeff Delaune, NASA Jet Propulsion Lab
Xin Chen, HERE Maps
Sebastian Scherer, CMU
Davide Scaramuzza, University of Zurich
Christopher Kanan, Rochester Institute of Technology
Anelia Angelova, Google Brain
Andreas Geiger, MPI
Peidong Liu, ETH
Hongdong Li, Australian National University
Hui Zhou,
Keith Sullivan, Naval Research Lab
John Galeotti, CMU
Kurt Konolige, Google X
Manoranjan Majji, Texas A&M University
Feras Dayoub, Queensland University of Technology
Ioannis Gkioulekas, CMU
Andreas Savakis, Rochester Institute of Technology

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