IPMV 2022

2022 4th International Conference on Image Processing and Machine Vision (IPMV 2022) is scheduled to be held in Hong Kong on March 25-27, 2022. As you have been aware, COVID-19 is still out of control for many countries, and the safety and well-being of our participants is of paramount importance to us. Therefore, after serious consideration, the committee decides to have IPMV 2021 as virtual mode. All accepted papers were presented by online.

IPMV 2022 conference proceedings have been archived in ACM Digital Library, and indexed by Ei Comendex and Scopus.


The organzing committees have invited Prof. James Tin-YauKwok (Fellow of IEEE, The Hong Kong University of Science and Technology, Hong Kong, China), Prof. Zhongfei(Mark) Zhang (Fellow of IEEE, Binghamton University State University of New York, USA) and Prof. Arumugam Nallanathan (Fellow of IEEE, Queen Mary University of London, UK) to deliver keynote talks in IPMV online conference.


There are 4 parallel sessions in IPMV 2022 conference with topics: Object Detection and Recognition, Computer Vision and Image Processing, Electronics and Control Systems, Modern Information System and Management. The best presentations are selected in each session, as followings:

Session 1: Object Detection and Recognition
"Masked Face Recognition with 3D Facial Geometric Attributes"
Yuan Wang, Hefei University of Technology, China
Session 2: Electronics and Control Systems
"Effect of Irradiation (SRFE) on MSM Photodetectors Device"
Itsara Srithanachai, King Mongkut's Institute of Technology Ladkrabang, Thailand
Session 3: Computer Vision and Image Processing
"A Quantitative Comparison of Automated Cleaning Techniques for Web Scraped Image Data of 'Smart Cities'"
Bob de Witte, University of Amsterdam, Netherlands


Session 4: Modern Information System and Management
"Countering IDS-based Tactical Voting in a Heterogenous Distributed IoT System"
Hamid Al-Hamadi, Kuwait University, Kuwait



"Adaptive, Automatic and Non-invasive Cultural Heritage Preventive Conservation Framework based on Visual Information Crowdsourcing"
Miguel Antonio Barbero-Álvarez, Universidad Politécnica de Madrid, Spain