ATTENTION: We're pleased to announce that EI 2021 will be fully online! We recognize that one of the most important features of attending EI is interacting with colleagues from Industry and Academia. We are committed to making community connection a priority and to providing ample opportunities to meet and discuss personally with others. We see the move to an online platform as an opportunity for greater numbers of people to join from around the world. Join us on this exciting adventure . . . submit your work today!
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.
2021 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
2021 Special Sessions
For Intelligent Robotics and Industrial Applications using Computer Vision 2021, submissions are particularly encouraged for the planned automation in production session.
Automation in Production
Session Organizing Chair:
Kurt Niel, Upper Austria University of Applied Sciences (Austria)
Within the automation process in production lines sensors have to check the physical behavior of objects for proper treatment. Image sensors are used for identification, metrology, and quality control. Examples are: manufacturing of cars (e.g. crack detection on motor blocks, completeness after assembly, etc.), print production (e.g. color check, position control, long term stability, etc.), guiding public traffic (e.g. check traffic flow, vehicle type identification, control pedestrian crossing, etc.). Within this session, the latest developments in reliable sensor fusion, illumination techniques, and metrology needs, are shown and discussed keeping in mind the given need in robustness, easy to handle, long term usage.
Awards
Best Student Paper Award
Past winners
2021 Committee
Conference Chairs
Kurt Niel, Upper Austria University of Applied Sciences (Austria)
Juha Röning, University of Oulu (Finland)
Program Committee
Philip Bingham, Oak Ridge National Laboratory (United States)
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)
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)
Sreenath Rao Vantaram, Apple Inc. (United States)
Seung-Chul Yoon, United States Department of Agriculture Agricultural Research Service (United States)
Gerald Zauner, FH OÖ– Forschungs Entwicklungs GmbH (Austria)
Dili Zhang, Monotype Imaging (United States)