- - - SUBMISSION DEADLINE EXTENDED UNTIL 30TH OF SEPTEMBER 2018! - - -
We are proud to present the 1st International Workshop on Advanced Machine Vision for Real-life and Industrially Relevant Applications (AMV2018). This workshop is in conjunction with ACCV2018, Perth, Australia and is scheduled on Monday the 3rd of December 2018 (all-day workshop).
A large variety of industrially oriented applications (e.g. quality control, pick and place) have in the past decades been successfully implemented throughout a wide range of industries. These implementations are characterized by very controlled surroundings and objects (e.g. CAD models of objects available, controlled lighting). Advanced Machine Vision refers to computer vision - based systems where such assumptions do not hold, for example, when handling biological objects as seen in the food-production industry or when operating outdoors. With recent advancements in sensing and processing power, the potential for further automation in industry based on computer vision is clearly present. Furthermore, the exploding domain of computer vision algorithms (e.g. deep learning) provides dozens of new opportunities. However, there is in general a major gap between the topics in focus at major international computer vision conferences and the actual industrial needs. More often approaches are hardly transferable into practical and robust solutions for industrial challenges. The ambition of this workshop is to close this gap, by bringing together both academics and practitioners from the field.
The workshop will take place at the conference venue: The Perth Convention and Exhibition Centre (PCEC), 21 Mounts Bay Road, Perth, Australia.
Please find a pdf version of this call for papers by clicking here
The ambition of this full-day AMV2018 workshop is to bring together practitioners and researchers from different disciplines related to Advanced Machine Vision to share ideas and methods on current and future use of computer vision algorithms in real-life and industrially relevant systems. This field raises the need of applied research that focusses on the technology transfer from academics towards practitioners, yielding several challenges like top-notch accuracies, real-time processing, minimal training data, minimal manual input, user-friendly interfaces, …
To this end we welcome contributions with a strong focus on (but not limited to) the following topics within Advanced Machine Vision:
Improving robustness of algorithms
Removing or reducing the need of training data
Processing power and memory requirements
Obtaining training data and ground truth annotations
Lab testing versus inline testing
Transfer learning towards new applicational domains
Deep learning for advanced machine vision
Quality assessment of non-trivial objects
Real-life and industrially relevant applications
The workshop has a best paper award of $1000 sponsored by iCetana. On top of that there will be a special issue of the Machine Vision and Applications journal following the workshop. This special issue will be for both extended workshop papers as well as an open call.
Authors are encouraged to submit high-quality, original (i.e. not been previously published or accepted for publication in substantially similar form in any peer-reviewed venue including journal, conference or workshop) research.
The paper template is identical to the main ACCV2018 conference:
Papers are limited to 14 pages, excluding references. The review process is double blind. Papers that are not blind, or do not use the template, or have more than 14 pages (excluding references) will be rejected without review. For details concerning the blind review we refer to the example submission paper linked above. All workshop submissions follow the policies of ACCV 2018 (found here).
Submissions are handled through the CMT submission website: CLICK HERE FOR SUBMISSION
Authors are requested to submit their paper in a single PDF file (maximum file size 20MB). Submission of supplementary material is optional (up to 100MB). Accepted file formats for supplementary material are: PDF, PNG, JPG, GIF, ZIP, MP4, WMV, MPEG or AVI.
All accepted workshop papers will be published as workshop post-proceedings in the Lecture Notes in Computer Science (LNCS) by Springer, after the actual meeting, in order to exclude no-show papers from publication. On top of that, questions and feedback raised at the workshop can be used on top of the review comments to improve the camera-ready submission.
On top of that there will be a special issue of the Machine Vision and Applications journal following the workshop. This special issue will be for both extended workshop papers as well as an open call.
For questions/remarks regarding the submission e-mail: email@example.com.
- List of reviewers to be added -
The organizing committee would like to thank all members of the program committee for the work they invest in assuring that our AMV2018 workshop achieves a high-quality standard!
The program itself is still under construction and will be made publicly available in due time.
We can already confirm the following invited speakers:
Environmental informatics studies new knowledge, technologies and devices for automation in agriculture and aquaculture, early detection of pest and plant disease, automatic species identification, plant phenomics, better water resource management, land environment monitoring, costal environment monitoring, marine life surveillance, etc. In this talk, he will introduce some of their work on automation in agriculture and aquaculture, faster grading and packing, species and cultivar identification, pest and disease recognition at Environmental Informatics @ Griffith, including recognition without detection, large image database retrieval (speed vs accuracy), and pose difference.
Professor Yongsheng Gao is the Director of Australian Research Council (ARC) Research Hub for Driving Farming Productivity and Disease Prevention, the founding Leader of the Environmental Informatics flagship group and the Director of Computer Vision and Image Processing Research Lab at Griffith University. He served as the project leader of Biosecurity Group, National ICT Australia (ARC Centre of Excellence) from 2009 to 2011. He is a current member of College of Experts (Panel Member), Australian Research Council. As a Chief Investigator, he has been working on projects in Australia, Singapore, Germany, and China in the areas of smart farming, biosecurity, face recognition, biometrics, image retrieval, computer vision, pattern recognition, environmental informatics, and medical imaging. He was also employed as a consultant by Panasonic Singapore Laboratories Pte Ltd working on the face recognition standard in MPEG-7. His research are reported in the media in Australia and Singapore, including The Australian, The Courier Mail, The Sydney Morning Herald, and The Straits Times (Singapore).
If anomaly detection is just about learning the normal and detecting abnormal activities, why is it so hard to employ in the real world? While it is straightforward for us to spot the unusual or discover strange activities, this task is quite challenging for computers. The meaning of what constitutes an anomaly changes for every environment, with weather, traditions and social behaviour playing important roles. This brings us a mystery to solve, which is something that we love and it is the heart of anomaly detection. However, unlike mystery/thriller movies, in anomaly detection, the problem to solve is not always clear, and we may not even know what we are trying to solve. This leads us to the main question to be discussed in this talk: what are the main challenges in implementing anomaly detection techniques that can employed in the wild, and what can we expect for the future of such algorithms?
Dr. Moussa Reda Mansour is the Research & Development Lead at iCetana, a world leader in AI assisted video anomaly detection. He received his Ph.D degree in electrical and computer engineering from the University of Sao Paulo, Brazil, in 2013. In the same year, he started a research fellow position at Institute of Mathematical and Computer Sciences (ICMC-USP), in which he led projects in machine learning, computer vision, data visualisation and graph theory. Later, as a research fellow at the University of Illinois at Urbana-Champaign, Dr. Reda Mansour worked with data visualisation and machine learning applied in large social networks with textual information. Currently, his main areas of research are Computer Vision, Unsupervised Learning, Deep Learning and Dynamical Systems applied in Learning Tasks.