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CCVPR 2020 : 2020 3rd International Joint Conference on Computer Vision and Pattern Recognition (CCVPR 2020)

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Link: http://www.ccvpr.org/
 
When Sep 21, 2020 - Sep 23, 2020
Where Saint Petersburg, Russia
Submission Deadline Jul 31, 2020
Notification Due Jul 31, 2020
Final Version Due Jul 31, 2020
Categories    computer vision   pattern recognition
 

Call For Papers

●2020 3rd International Joint Conference on Computer Vision and Pattern Recognition (CCVPR 2020)-- Ei Compendex & Scopus—Call for papers 
|September 21-23, 2020|Saint Petersburg, Russia|Website: http://www.ccvpr.org/

●CCVPR 2020 welcomes researchers, engineers, scientists and industry professionals to an open forum where advances in the field of Computer Vision and Pattern Recognition can be shared and examined. The conference is an ideal platform for keeping up with advances and changes to a consistently morphing field.

●Publication and Indexing
All accepted papers will be published in the digital conference proceedings which will be Indexed by  all major citation databases such as  Conference Proceedings Citation Index – Science (CPCI-S),(Thomson Reuters, Web of Science), Scopus, Ei Compendex, Inspec, INIS (International Nuclear Information System), Chemical Abstracts, NASA Astrophysics Data System, Polymer Library, etc.
A selection of best papers with extended versions will be recommended to publish in journals. 

●Keynote Speakers
Prof. Ian Robert McAndrew, Capitol Technology University, Maryland, USA

●Program Preview/ Program at a glance
September 21, 2020: Registration + Icebreaker Reception
September 22, 2020: Opening Ceremony+ KN Speech+ Technical Sessions 
September 23, 2020: Technical Sessions+ Half day tour/Lab tours

●Paper Submission
1. PDF version submit via CMT: https://cmt3.research.microsoft.com/CCVPR2020
2. Submit Via email directly to: ccvpr@iased.org

●CONTACT US
Ms. Kiki.Y.P.Kwok
Email: ccvpr@iased.org
Website: http://www.ccvpr.org/

Call for papers(http://www.ccvpr.org/to.html):
IMAGE PROCESSING:
(Addresses ow-level processing as well as imaging fundamentals.)
- 3D Imaging
- Enhancement Techniques
- Image Compression, Coding, and Encryption
- Image Data Structures and Databases
- Image Generation, Acquisition, and Processing
- Image Geometry and Multi-view Geometry
- Image Restoration
- Image-based Modeling and Algorithms
- Mathematical Morphology
- Motion and Tracking Algorithms and Applications
- Multimedia Systems and Applications
- Multi-resolution Imaging Techniques
- Novel Image Processing Applications
- Novel Noise Reduction Algorithms
- Performance Analysis and Evaluation
- Segmentation Techniques
- Software Tools for Imaging
- Video Analysis
- Watermarking Methods and Protection + Wavelet Methods
COMPUTER VISION:
(Addresses mid- to high-level processing as well as vision fundamentals.)
- Active and Robot Vision
- Biometric Authentication
- Camera Networks and Vision
- Cognitive and Biologically Inspired Vision
- Face and Gesture Recognition
- Fuzzy and Neural Techniques in Vision
- Image Feature Extraction
- Machine Learning Technologies for Vision
- Medical Image Processing and Analysis
- Novel Document Image Understanding Techniques
- Novel Vision Application and Case Studies
- Object Recognition
- Sensors and Early Vision
- Soft Computing Methods in Image Processing and Vision
- Special-purpose Machine Architectures for Vision
- Stereo Vision

PATTERN RECOGNITION:
(Addresses pattern recognition algorithms and methodologies that are of value to the image processing and computer vision research communities.)
- Applications Including: Security, Medicine, Robotic, GIS, Remote Sensing, Industrial Inspection, Nondestructive Evaluation (or NDE), ...
- Bayesian Methods in Pattern Recognition and Matching
- Case studies and Emerging technologies
- Clustering Techniques
- Dimensionality Reduction Methods in Pattern Recognition
- Ensemble Learning Algorithms
- Invariance in Pattern Recognition
- Knowledge-based Recognition
- Parsing Algorithms
- Statistical Pattern Recognition
- Structural and Syntactic Pattern Recognition
- Supervised and Un-supervised Classification Algorithms
- Symbolic Learning

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