RoLoD: Robust local descriptors for computer vision 2014

in conjunction with the 12th Asian Conference on Computer Vision (ACCV 2014)

Nov. 1-5, 2014, Singapore

News

1.         Paper submission for the special issue of the journal of neurocomputing is now open.

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

Submission

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.

 

Abstract

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.

 

Topics

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:

  1. New local descriptors robust to noise, illuminations, scale, rotations and occlusions,
  2. New applications of local descriptors in different domains, e.g. medical domain,
  3. Other application in different  domain, such as one dimension (1D) digital signal processing, 2D images, 3D videos and 4D videos,
  4. Evaluations of current local descriptors.
  5. Evaluations between the features learned by deep learning and the traditional descriptors (e.g., LBP, SIFT, HOG)

 

Motivation

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.

 

Important Dates

  1. Paper Submission: Sept. 10th, 2014, Sept. 15th, 2014
  2. Notification of acceptance: Sept. 25th, 2014,
  3. Camera-ready paper: Oct. 1th, 2014
  4. Workshop: Nov. 1th, 2014

 

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.

 

Workshop Chairs

 

Invited Speakers:

Topic: PASCAL VOC Classification: Local Features vs. Deep Features

Prof.  Shuicheng Yan

National University of Singapore

 

 

Program Committee:

·           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

 

 

Contact

Jie Chen

Email: rolod2014@gmail.com

Center for Machine Vision Research (CMV),

University of Oulu, Finland

 

 

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