We are organizing a special session at the World Congress on Computational Intelligence (WCCI) 2016 titled “Fuzzy Set Theory in Computer Vision” (FUZZ-IEEE-13). We hope that you will consider being an IPC member for our special session and possibly submit an article (and/or forward this announcement to anyone else you think might be interested in submitting an article). Please let us know if you will be a member (with an absolute maximum of three articles to review). Note, if you accept this invitation then you will need to select our specific special session in the review system as your first topic so everything is properly linked in the system.
Conference website: http://www.wcci2016.org/
Special session link: http://www.wcci2016.org/spsessions.php
Important dates:
Paper submission: January 15th, 2016
Notification of acceptance: March 15th, 2016
Final paper submission: April 15th, 2016
Early registration deadline: April 15th, 2016
Conference: July 25-29, 2016
Instructions for authors, submission and more details in the conference website:
Accepted papers to this special session (if presented at the Conference) will be published in the conference proceedings of FUZZ-IEEE published by the IEEE.
Thank you and hope to see you in Vancouver!
Organizing Committee
Derek T. Anderson, Mississippi State University, USA [anderson@ece.msstate.edu]
Chee Seng Chan, University of Malaya, Malaysia [cs.chan@um.edu.my]
James M. Keller, University of Missouri, USA [kellerj@missouri.edu]
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Title:
Fuzzy Set Theory in Computer Vision
Aim and Scope:
Fuzzy set theory is the subject of intense investigation in fields like control theory, robotics, biomedical engineering, computing with words, knowledge discovery, remote sensing and socioeconomics, to name a few. However, in the area of computer vision, other fields, e.g., machine learning, and communities, e.g., PAMI, ICCV, CVPR, ECCV, NIPS, are arguably state-of-the-art. In particular, the vast majority of top performing techniques on public datasets are steeped in probability theory. Important questions to the fuzzy set community include the following. What is the role of fuzzy set theory in computer vision? Does fuzzy set theory make the most sense and biggest impact in terms of low-, mid- or high-level computer vision? Furthermore, do current performance measures favor machine learning approaches? Last, is there additional benefit that fuzzy set theory brings, and if so, how is it measured?
This special session invites new research in fuzzy set theory in computer vision. It is a follow up to the 2013 FUZZ-IEEE workshop View of Computer Vision Research and Challenges for the Fuzzy Set Community and Fuzzy Set Theory in Computer Vision special sessions in 2014 and 2015. In particular, we encourage authors to investigate their research using public datasets and to compare their results to both fuzzy and non-fuzzy methods. Topics of interest include all areas in computer vision and image/video understanding. Example topics include, but are definitely not limited to, the following:
- Detection and recognition
- Categorization, classification, indexing and matching
- 3D-based computer vision
- Advanced image features and descriptors
- Motion analysis and tracking
- Linguistic description and summarization
- Video: events, activities and surveillance
- Intelligent change detection
- Face and gesture
- Low-level, mid-level and high-level computer vision
- Data fusion for computer vision
- Medical and biological image analysis
- Vision for Robotics