CFP：RoF: Robust Features for Computer Vision in conjunction with CVPR2016
http://www.ee.oulu.fi/~jiechen/CVPR2016_Workshop_RoF.htm?from=groupmessage&isappinstalled=0RoF: Robust Features for Computer Vision
in conjunction with CVPR2016Las Vegas, Nevada, June 26, 2016Submission1. Paper submission will be open2. The authors will submit full length papers (CVPR format) on-line, including (1) Title of paper & short abstract summarizing the main contribution, (2) Contributions must be written and presented in English, and (3) The paper in PDF format. All submissions will be peer-reviewed by at least 3 members of the program committee. TopicsWe encourage researchers to develop new robust features (e.g., manually designed local features or feature learning) to extract useful feature representations. We also encourage new theories and processes related to features. We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to:
[*]New features (manually designed local features, or feature learning in supervised, weakly supervised or unsupervised way) robust to noise, illuminations, scale, rotations and occlusions,
[*]Robust features that are suitable for wearable devices (e.g., smart glasses, smart phones) with strict requirements for computational efficiency and low power consumption,
[*]New applications of descriptors in different domains, e.g. medical domain,
[*]Other application in differentdomain, such as one dimension (1D) digital signal processing, 2D images, 3D videos and 4D videos,
[*]Evaluations of current traditional descriptors,
[*]Evaluations between the features learned by deep learning and the traditional descriptors (e.g., LBP, SIFT, HOG)
MotivationThe goal of the RoF Workshop 2016 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
[*]Paper Submission (tentative): March 8, 2016
[*]Notification of acceptance: April 20, 2016
[*]Camera-ready paper: May 2nd, 2016
[*]Workshop (half day): July 1, 2016
Workshop Chairs
[*]Jie Chen, CMV, Finland
[*]Zhen Lei NLPR, CASIA, China
[*]Li Liu, VIP, Canada
[*]Guoying Zhao, CMV, Finland
[*]Matti Pietikäinen, CMV, Finland
Invited Speakers:1) Deep features for face recognition and related problemsProf. Rama Chellappa, University of Maryland, USAThis talk will cover their recent work on using deep features for face verification, recognition, age estimation, face detection, alignment and expression recognition. 2) Convolutional patch representations for image retrievalDr. Cordelia Schmid, INRIA, FRANCE 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 ContactJie ChenEmail: rolod2014@gmail.comCenter for Machine Vision Research (CMV),University of Oulu, Finland
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