5th international conference on Pattern Recognition and Image Analysis (2021) at University of Kashan

About IPRIA 2021

The International conference on Pattern Recognition and Image Analysis (IPRIA) is an excellent and premier forum comprising several scientific tracks organized by Iranian Society of Machine Vision and Image Processing (ISMVIP). The 5th IPRIA conference will be held at the Department of Electrical and Computer Engineering, University of Kashan, Kashan, Iran, April 28–29, 2021.

On behalf of the IPRIA 2021 organizing committee, it is our great pleasure to invite you to the bi-annual International conference on Pattern Recognition and Image Analysis.    

Call for Paper

Topics of interest at IPRIA 2021 include, but are not restricted to:

  • 3D Computer Vision
  • Activity Recognition
  • Biomedical Image Processing
  • Compression and Watermarking
  • RGBD processing
  • Image and Video Mining
  • Motion Analysis and Tracking
  • Stereo and Multi-view Geometry
  • Texture, Shape, and Color
  • Document Processing and Recognition
  • Machine Learning Techniques in Vision
  • Deep Learning in Computer Vision
  • Computer Vision and Image Processing Architectures
  • Quantum Image Processing
  • Other related topics

Read more ...


Accepted papers will be submitted to IEEE Xplore

Keynote speaker, Dr. Mohammad Shokoohi-Yekta

Keynote speaker, Professor Shohreh Kasaei

Keynote speaker, Dr Gerald Schaefer

All News


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Paper submission: December 1, 2020


Acceptance notification: January 1,2021


Camera ready due: February 1,2021


Conference: April 28–29, 2021



Keynote speakers

Professor Sergios Theodoridis
National and Kapodistrian University of Athen, GR
Topic of Talk:  Bayesian Machine Learning

Dr Gerald Schaefer
Loughborough University, UK
Topic of Talk: Latest Developments in Computer Vision



Professor Shohreh Kasaei
Sharif University of Technology, IR
Topic of Talk: Advances in 3D Computer Vision

Dr. Mohammad Shokoohi-Yekta
Senior Data & Applied Scientist at Microsoft
and Instructor at Stanford University, CA, USA
Topic of Talk: Hottest Trends and Challenges of Deep Learning


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