Journal-first (JIST/JPI) Submissions

∙ Journal-first (JIST/JPI) Submissions Due 31 July
∙ Final Journal-first manuscripts due 31 Oct
Conference Papers Submissions
∙ Late Submission Deadline
15 Oct
∙ FastTrack Proceedings Manuscripts Due 8 Jan 2024
∙ All Outstanding Manuscripts Due 15 Feb 2024
Registration Opens mid-Oct
Demonstration Applications Due 21 Dec
Early Registration Ends 18 Dec

Hotel Reservation Deadline 10 Jan
Symposium Begins
21 Jan
Non-FastTrack Proceedings Manuscripts Due
15 Feb

Intelligent Robotics and Industrial Applications Using Computer Vision 2024

Conference keywords: intelligent robots, industrial inspection, computer vision, sensing and imaging techniques, sensor fusion

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Conference Overview

This conference brings together real-world practitioners and researchers in intelligent robots and computer vision to share recent applications and developments. Topics of interest include the integration of imaging sensors supporting hardware, computers, and algorithms for intelligent robots, manufacturing inspection, characterization, and/or control.

The decreased cost of computational power and vision sensors has motivated the rapid proliferation of machine vision technology in a variety of industries, including aluminum, automotive, forest products, textiles, glass, steel, metal casting, aircraft, chemicals, food, fishing, agriculture, archaeological products, medical products, artistic products, etc. Other industries, such as semiconductor and electronics manufacturing, have been employing machine vision technology for several decades. Machine vision supporting handling robots is another main topic. With respect to intelligent robotics another approach is sensor fusion – combining multi-modal sensors in audio, location, image and video data for signal processing, machine learning and computer vision, and additionally other 3D capturing devices.

There is a need for accurate, fast, and robust detection of objects and their position in space. Their surface, background, and illumination are uncontrolled, and in most cases the objects of interest are within a bulk of many others. For both new and existing industrial users of machine vision, there are numerous innovative methods to improve productivity, quality, and compliance with product standards. There are several broad problem areas that have received significant attention in recent years. For example, some industries are collecting enormous amounts of image data from product monitoring systems. New and efficient methods are required to extract insight and to perform process diagnostics based on this historical record. Regarding the physical scale of the measurements, microscopy techniques are nearing resolution limits in fields such as semiconductors, biology, and other nano-scale technologies. Techniques such as resolution enhancement, model-based methods, and statistical imaging may provide the means to extend these systems beyond current capabilities. Furthermore, obtaining real-time and robust measurements in-line or at-line in harsh industrial environments is a challenge for machine vision researchers, especially when the manufacturer cannot make significant changes to their facility or process.

2024 Conference Topics

  • Computer vision algorithms and applications for industry, intelligent robots, surveillance
  • Pattern recognition and image processing for computer vision and robotics
  • 3D vision: modeling, representation, perception, processing, and recognition; predictive 3D vision
  • Non-classical sensing, sensor fusion
  • Physical, thermal, color, and/or spectroscopic imaging algorithms and applications
  • Novel hardware designs / vision system architectures
  • Machine vision for process control/diagnosis, trend analysis, or preventative maintenance
  • High-throughput systems for medical or biological applications
  • Machine vision applications for industrial research and development, inspection in harsh environments
  • Intelligent packaging, processing, and material handling
  • Intelligent mobile robot methods and advancements (tracking, scene analysis, path planning, obstacles)
  • Autonomous multi-vehicle collaboration (including UAVs)
  • Robotic aids for the elderly, health, education and transportation
  • Cognitive learning strategies and systems (intelligent robots that adapt, learn, and manage complexity)
  • Tracking and scene analysis for intelligent vehicles
  • Segmentation for object location and obstacle avoidance for intelligent robots
  • Active vision and real-time techniques
  • Image understanding and scene analysis for intelligent robots
  • Object modeling and recognition for robotic vision
  • Computational attention
  • Mobile mapping

2024 Special Sessions

AI in Industrial Machine Vision Applications

Session Organizing Chair:
Gerald Zauner, University of Applied Sciences Upper Austria

Do AI systems fulfill the requirements of industrial reliability in time and quality?


Best Student Paper Award

Past winners

2020 Best Student Paper
Sander Klomp and Dennis van de Wouw (Eindhoven University of Technology) for their work on "Real-time small-object change detection from ground vehicles using a Siamese convolutional neural network." After EI 2020, this work was chosen for an IS&T Seminar Series: Best Student Research presentation.
2019 Best Student Paper
Sidhant Gupta (The University of Hong Kong) for his work titled "Laser quadrant and photogrammetry based autonomous coral reef mapping ocean robot."
2018 Best Student Paper
Edward Scott (Northeastern University) for his work on  "No-reference utility estimation with a convolutional neural network."
2017 Best Paper
Can Erhan, Evangelos Sariyanidi, Onur Sencan, and Hakan Temeltas (Instanbul Technical University and Queen Mary University of London) for their work titled "Efficient visual loop closure detection in different times of day."
2017 Best Student Paper
Doris Antensteiner (AIT Austrian Institute of Technology GmbH) for her work on  "High-precision 3D sensing with hybrid light field and photometric stereo approach in multi-line scan framework."
2017 Best Student Paper
Tian Zhou (Purdue University) for his work on "Finding a needle in a haystack: Recognizing surgical instruments through vision and manipulation."
2016 Best Paper
Yusuke Nakayama, Hideo Saito, Masayoshi Shimizu, and Nobuyasu Yamaguchi (Keio Univeristy and Fujitsu Laboratories Ltd.) for their work titled "Marker-less augmented reality framework using on-site 3D line segment based model generation."

2024 Committee

Conference Chairs

Kurt Niel, University of Applied Sciences Upper Austria (Austria)
Juha Röning, University of Oulu (Finland)
Doris Antensteiner, Austrian Institute of Technology (Austria)

Program Committee

Philip Bingham, Oak Ridge National Laboratory (United States)
Christian Eitzinger, Profactor (Austria)
Ewald Fauster, Montan Universitat Leoben (Austria)
Steven Floeder, 3M Company (United States)
David Fofi, University de Bourgogne (France)
Shaun Gleason, Oak Ridge National Lab (United States)
B. Keith Jenkins, The University of Southern California (United States)
Christian Kapeller, Austrian Institute of Technology (Austria)
Olivier Laligant, University de Bourgogne (France)
Edmund Lam, The University of Hong Kong (Hong Kong, China)
Dah-Jye Lee, Brigham Young University (United States)
Junning Li, Keck School of Medicine, University of Southern California (United States)
Wei Liu, The University of Sheffield (United Kingdom)
Charles McPherson, Draper Laboratory (United States)
Fabrice Meriaudeau, University de Bourgogne (France)
Henry Y.T. Ngan, ENPS Hong Kong (China)
Lucas Paletta, JOANNEUM RESEARCH Forschungsgesellschaft mbH (Austria)
Vincent Paquit, Oak Ridge National Laboratory (United States)
Daniel Raviv, Florida Atlantic University (United States)
Hamed Sari-Sarraf, Texas Tech University (United States)
Ralph Seulin, University de Bourgogne (France)
Svorad Štolc, Photoneo (Slovakia)
Christophe Stolz, University de Bourgogne (France)
Bernard Theisen, U.S. Army Tank Automotive Research, Development and Engineering Center (United States)
Lukas Traxler, Austrian Institute of Technology (Austria)
Sreenath Rao Vantaram, Apple Inc. (United States)
Seung-Chul Yoon, United States Department of Agriculture Agricultural Research Service (United States)
Gerald Zauner, University of Applied Sciences Upper Austria (Austria)
Dili Zhang, Monotype Imaging (United States)

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