in conjunction with the 12th Asian Conference on Computer Vision (ACCV 2014)
Nov. 1-5, 2014, Singapore
2. Slides of the keynote speech by Dr. Shuicheng Yan.
3. Our workshop proposal was accepted by the journal of neurocomputing (SCI Impact Factor: 2.005).
· Those good papers accepted by ACCV2014 workshop will be recommended to this special issue.
· All manuscripts of this special issue will be submitted and reviewed via online Elsevier Editorial System (EES).
· Timeline for this special issue
Paper submission: January 1, 2015
Reviewing process and revision submission: July 1, 2015
Production process (online publication): November 1, 2015
4. Paper submission for the workshop is now open
1. Paper submission is now open
2. The authors will submit full length papers (ACCV format) on-line, including (1) Title of paper & short abstract summarizing the main contribution, (2) Names and contact info of all authors, also specifying the contact author, (3) Contributions must be written and presented in English, and (4) The paper in PDF format.
There has been much interest in object and view matching using local invariant features, classification of textured regions using micro textons and in face recognition using local features. How to extract robust representations for many computer vision tasks is still a challenging problem. This workshop will focus on the developing of new robust local descriptors to extract useful feature representations for these challenges.
We encourage researchers to develop new robust local descriptors to extract useful feature representations for these challenges. We also encourage new theories and processes related to local descriptors for dealing with these challenges. We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to:
The goal of the RoLoD Workshop 2014 is to accelerate the study of robustness of local descriptors in computer vision problems. With the increase of acceleration of digital photography and the advances in storage devices over the last decade, we have seen explosive growth in the available amount of visual data and equally explosive growth in the computational capacities for data understanding. How to extract robust representations for many computer vision tasks is still a challenging problem. This problem becomes more difficult when the data show different types of variations, e.g., noise, illuminations, scale, rotations and occlusions.
Paper Submission Information
The authors will submit full length papers (ACCV format) on-line, including (1) Title of paper & short abstract summarizing the main contribution, (2) Names and contact info of all authors, also specifying the contact author, (3) Contributions must be written and presented in English, and (4) The paper in PDF format. All submissions will be peer-reviewed by at least 3 members of the program committee.
Topic: PASCAL VOC Classification: Local Features vs. Deep Features
Prof. Shuicheng Yan
National University of Singapore
· Aleix Martinez, Ohio State University, USA
· Alice Caplier, Grenoble, France
· Bin Fan, Chinese Academy of Sciences, China
· Baochang Zhang Beihang University, China
· Engin Tola, Aurvis R&D, Turkey
· Enrique Alegre, University of León, Spain
· Francesca Odone, university of Genova, Italy
· Giovanni Fusco, Smith-Kettlewell Eye Research Institute, USA
· Huu Tuan NGUYEN, Grenoble, France
· Hazim Kemal Ekenel, Karlsruhe Institute of Technology, Germany
· Ioannis Patras Queen Mary University, UK
· Jean-Luc, Dugelay, Eurecom, France
· Juho Kannala, University of Oulu, Finland
· Jun Yang, Northwestern Polytechnical University, China
· Lijun Yin, Binghamton University, USA
· Lei Zhang, Hong Kong Polytechnic University, Hong Kong, China
· Loris Nanni, University of Padua (Padova), Italy
· Michael Teutsch, Fraunhofer IOSB, Germany
· Motilal Agrawal, Menlo Park, CA, USA
· Nicoletta Noceti, University of Genova, Italy
· Rainer Lienhart, Universität Augsburg, Germany
· Ruiping Wang, Chinese Academy of Sciences, China
· Rocio A Lizarraga-Morales, Universidad de Guanajuato DICIS, Mexico
· Sei-ichiro Kamata, Waseda University, Japan
· Shu Liao, Siemens, USA
· Shengcai Liao, NLPR, Chinese Academy of Sciences, China
· Tiago de Freitas Pereira, University of Campinas (UNICAMP), Brazil
· Tri Huynh, Eurecom, France
· Wenchao Zhang, Nanyang technological university, Singapore
· Xianbiao Qi, Beijing University of Posts and Telecommunications, China
· Xiaoyang Tan, Nanjing University of Aeronautics and Astronautics, China
· Xiujuan Chai, ICT, Chinese Academy of Sciences, China
· Xiaopeng Hong, University of Oulu, Finland
Center for Machine Vision Research (CMV),
University of Oulu, Finland
Any question about this page? Turn to its host!
This page is maintained currently by jie chen. Last modified: 2010-2-11, 11:47