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ACM IPMV 2020 : 2nd International Conference on Image Processing and Machine Vision - EI Compendex, Scopus

Bangkok, Thailand
Event Date: Aug 05, 2020 - Aug 07, 2020
Submission Deadline: Mar 30, 2020
Notification of Acceptance: Apr 20, 2020

About

This conference is meant for researchers from academia, industries and research & development organizations all over the globe interested in the areas of machine learning methods/ algorithms, signal processing theory and methods, data mining, artificial intelligence, optimization and applications to human brain disorders like epilepsy etc. Other applications of image processing and machine vision techniques are also welcome. It will put special emphasis on the participations of PhD students, Postdoctoral fellows and other young researchers from all over the world. It would be beneficial to bring together a group of experts from diverse fields to discuss recent progress and to share ideas on open questions. The conference will feature world-class keynote speakers in the main areas. Image Processing and Machine Vision usually plays an important role in the transition from data storage to decision systems based on largeĀ  databases of signals such as the obtained from sensor networks, internet services, or communication systems. These systems imply developing both computational solutions and novel models. Signals from real-world systems are usually complex such as speech, music, bio-medical, multimedia, among others. Thus, Image Processing and Machine Vision techniques are very useful for these type of systems to automate processing and analysis techniques to retrieve information from data storage.

Call For Paper

Authors are invited to submit full-length high-quality papers in image processing and machine vision. Papers covering theory and/or application areas of computer vision are invited for submission. Submitted papers will be refereed on their originality, presentation, empirical results, and quality of evaluation.

Topics include, but are not limited to:

  • Statistics and machine learning for vision
  • Stereo, calibration, geometric modelling and processing
  • Face and gesture recognition
  • Early and biologically inspired vision
  • Motion, flow and tracking
  • Segmentation and grouping
  • Model-based vision
  • Image processing techniques and methods
  • Texture, shape and colour
  • Video analysis
  • Document processing and recognition
  • Vision for quality assurance, medical diagnosis, etc.
  • Vision for visualization, interaction, and graphics
  • Object detection and recognition
  • Shape-from-X
  • Video analysis for action and event recognition
  • Illumination and reflectance
  • Deep learning for vision
  • 3D computer vision
  • RGBD analysis

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