Call for papers in a special session: “Analyzing nondestructive evaluation data. Is it automated yet?” – ISPA2021

Extended paper submission

12th International Symposium on Image and Signal Processing and Analysis (ISPA2021):
https://www.isispa.org/
13-15th September 2021, Zagreb, Croatia

Special session Announcement

We are organizing a special session on automated analysis of the nondestructive evaluation data at the 12th International Symposium on Image and Signal Processing and Analysis (ISPA 2021). The symposium will be held in Zagreb, Croatia, on September 13-15, 2021 in a hybrid format so both physical and virtual attendance is possible. Depending on the situation with the Covid-19 pandemic the conference may shift to an online-only format.

We are inviting you to submit a paper for this special session. You may find a short description and the technical scope of this session on the following URL: 

If you are interested, please submit your paper by the 31st of May June 13th following the instructions on our official conference website. Note that this deadline might be extended in case we receive a number of requests from the authors who are not able to meet the current deadline. Feel free to share this invitation with your colleagues.


Special Session chairs

Luka Posilović, University of Zagreb, Croatia

Duje Medak, University of Zagreb, Croatia

ICCV 2021 Workshop: 2nd Autonomous Vehicle Vision (AVVision)

[CfP] ICCV 2021 Workshop: 2nd Autonomous Vehicle Vision (AVVision)

 

The 2nd Autonomous Vehicle Vision (AVVision) Workshop aims to bring together industry professionals and academics to brainstorm and exchange ideas on the advancement of computer vision techniques for autonomous driving. In this one-day workshop, we will have seven keynote talks and regular paper presentations (oral and poster) to discuss the state of the art as well as existing challenges in autonomous driving. The workshop webpage is at https://avvision.xyz/iccv21/.

 

Keynote Speakers:

 

  • Cordelia Schmid, INRIA
  • Raquel Urtasun, University of Toronto
  • Andreas Geiger, University of Tübingen
  • Fisher Yu, ETH Zürich
  • Laura Leal-Taixé, Technical University of Munich
  • Matthew Johnson-Roberson, University of Michigan
  • Carl Wellington, Aurora

 

Call for papers:

 

With a number of breakthroughs in autonomous system technology over the past decade, the race to commercialize self-driving cars has become fiercer than ever. The integration of advanced sensing, computer vision, signal/image processing, and machine/deep learning into autonomous vehicles enables them to perceive the environment intelligently and navigate safely. Autonomous driving is required to ensure safe, reliable, and efficient automated mobility in complex uncontrolled real-world environments. Various applications range from automated transportation and farming to public safety and environment exploration. Visual perception is a critical component of autonomous driving. Enabling technologies include: a) affordable sensors that can acquire useful data under varying environmental conditions, b) reliable simultaneous localization and mapping, c) machine learning that can effectively handle varying real-world conditions and unforeseen events, as well as “machine-learning friendly” signal processing to enable more effective classification and decision making, d) hardware and software co-design for efficient real-time performance, e) resilient and robust platforms that can withstand adversarial attacks and failures, and f) end-to-end system integration of sensing, computer vision, signal/image processing and machine/deep learning. The 2nd AVVision workshop will cover all these topics. Research papers are solicited in, but not limited to, the following topics:

 

  • 3D road/environment reconstruction and understanding;
  • Mapping and localization for autonomous cars;
  • Semantic/instance driving scene segmentation and semantic mapping;
  • Self-supervised/unsupervised visual environment perception;
  • Car/pedestrian/object/obstacle detection/tracking and 3D localization;
  • Car/license plate/road sign detection and recognition;
  • Driver status monitoring and human-car interfaces;
  • Deep/machine learning and image analysis for car perception;
  • Adversarial domain adaptation for autonomous driving;
  • On-board embedded visual perception systems;
  • Bio-inspired vision sensing for car perception;
  • Real-time deep learning inference.

 

Important Dates:

 

  • Paper submission deadline: Jul. 23, 2021
  • Review feedback release date: Aug. 09, 2021
  • Camera-ready Submission: Aug. 16, 2021
  • Workshop date: Oct. 10-17, 2021 (TBD)

 

Submission Guidelines:

 

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 ICCV 2021 main conference. Papers are limited to eight pages, including figures and tables, in the ICCV style. Additional pages containing only cited references are allowed. Please refer to the following files for detailed formatting instructions:

 

  • Example submission paper with detailed instructions Download;
  • LaTeX Templates (zip): iccv2021AuthorKit.zip Download

 

Papers that are not properly anonymized, or do not use the template, or have more than eight pages (excluding references) will be rejected without review. The submission site is now open.

 

Organizers:

 

  • Rui Ranger Fan, UC San Diego
  • Nemanja Djuric, Aurora
  • Rowan McAllister, Toyota Research Institute
  • Ioannis Pitas, Aristotle University of Thessaloniki 

 

 

Last call: Invitation to join 2021 Spring School ‘CVML Short Course – Computer Vision for Autonomous Systems’, 26-27th May 2021 (6th edition)

you are welcomed to register in this CVML Short e-course on ‘Computer Vision for Autonomous Systems’, 2627th May 2021: https://icarus.csd.auth.gr/spring-cvml-short-course-computer-vision-for-autonomous-systems/

 

It will take place as a two-day e-course (due to COVID-19 circumstances), hosted by the Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece, providing a series of live lectures delivered through a tele-education platform. They will be complemented with on-line video recorded lectures and lecture pdfs, to facilitate international participants having time difference issues and to enable you to study at own pace.  You can also self-assess your knowledge, by filling appropriate questionnaires (one per lecture). You will be provided programming exercises to improve your programming skills.

It is part of the very successful CVML short course series that took place in the last three years.

 

The short e-course consists of 16 1-hour live lectures organized in two Parts (1 Part per day):

Part A lectures (8 hours) provide an in-depth presentation of  2D and 3D Computer Vision theory and applications in the above-mentioned diverse domains, primarily for semantic 3D world modeling and localization. Computer Vision starts with a detailed presentation of digital image/video fundamentals and image acquisition and camera geometry, including camera calibration. Then, two lectures on: a) Stereo and Multiview imaging and b) Structure from motion will provide the theoretical and algorithmic tools to recover 3D world models from images. They will be used on Localization and mapping that is of primary importance in Autonomous Systems and Robotic perception. This is complemented by Neural techniques for recovering depth information and 3D world modeling, even from monocular images. Deep semantic image segmentation will conclude this part, by providing DNN methods both to label and segment regions, e.g., roads and targets, e.g., cars, pedestrians.

Part B lectures (8 hours) will start with an overview of Autonomous Systems Sensors. Then , it will provide an in-depth presentation of Computer Vision theory and applications in autonomous systems, particularly as related to target detection, tracking and object pose estimation. Applications will be presented for Multiple Drone Systems Autonomous Car Vision and Autonomous Marine Surface Vessels. Finally, CVML programming tools (e.g., DNN frameworks, BLAS/cuBLAS, DNN and CV libraries) are overviewed, as they allow fast application of all the above knowledge in almost any application domain.

 

Course lectures

Part A: Computer Vision (first day, 8 lectures)
  1. Digital images and videos
  2. Image Acquisition. Camera Geometry
  3. Stereo and Multiview Imaging
  4. Structure from Motion
  5. 3D Robot Localization and Mapping
  6. Neural 3D world modeling
  7. Image/Point cloud registration
  8. Deep semantic image segmentation
Part B: Autonomous Systems (second day, 8 lectures)
  1. Autonomous Systems Sensors
  2. Deep object detection
  3. Object Tracking
  4. Object Pose Estimation
  5. Multiple Drone Systems
  6. Autonomous Car Vision
  7. Autonomous Surface Vessels
  8. CVML Software Development Tools

 

Though independent, the attendees of this short e-course will greatly benefit by attending the CVML Short e-course on ‘Machine Learning and Deep Neural Networks’ 27-28th April 2021:

http://icarus.csd.auth.gr/spring-cvml-short-course-machine-learning-and-deep-neural-networks/

 

You can use the following link for course registration:

http://icarus.csd.auth.gr/spring-cvml-short-course-computer-vision-for-autonomous-systems/

Up to 10 PhD students, registered in AUTH or in any AI4Media or ELISE or Humane-AI-Net or VISION CSA or TAILOR University partners, are entitled for 1 free CVML Web Course registration per fall/spring semester on a FCFS basis, with priority to ones working on AI-related topics. This offer is related to the upcoming educational activities of International AI Doctoral Academy (AIDA) that is co-initiated by these two projects.

Please send email with title «Spring School 2021 – Computer Vision for Autonomous Systems: free registration» to koroniioanna@csd.auth.gr, if you belong to the above category.

Lecture topics, sample lecture ppts and videos, self-assessment questionnaires and programming exercises can be found therein.

For questions, please contact: Ioanna Koroni <koroniioanna@csd.auth.gr>

 

The short course is organized by Prof. I. Pitas, IEEE and EURASIP fellow, Chair of the IEEE SPS Autonomous Systems Initiative, Director of the Artificial Intelligence and Information analysis Lab (AIIA Lab), Aristotle University of Thessaloniki, Greece, Coordinator of the European Horizon2020 R&D project Multidrone. He is ranked 249-top Computer Science and Electronics scientist internationally by Guide2research (2018). He is head of the EC funded AI doctoral school of Horizon2020 EU funded R&D project AI4Media (1 of the 4 in Europe). He has 32200+ citations to his work and h-index 85+.

 

AUTH is ranked 153/182 internationally in Computer Science/Engineering, respectively, in USNews ranking.

 

Relevant links:

1) Prof. I. Pitas:

https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el

2) Horizon2020 EU funded R&D project Aerial-Core: https://aerial-core.eu/

3) Horizon2020 EU funded R&D project Multidrone: https://multidrone.eu/

4) Horizon2020 EU funded R&D project AI4Media: https://ai4media.eu/

5) AIIA Lab: https://aiia.csd.auth.gr/

 

Sincerely yours

Prof. I. Pitas

Director of the Artificial Intelligence and Information analysis Lab (AIIA Lab)

Aristotle University of Thessaloniki, Greece

 

DSEC Competition – Disparity/Depth prediction from Event Cameras for Driving Scenarios – Deadline 11th June

We are hosting a CVPR2021 workshop competition on Disparity/Depth
prediction from Event data for urban driving scenarios!

The goal is to estimate dense disparity from stereo event cameras and
stereo global shutter cameras in the event camera frame:
https://dsec.ifi.uzh.ch/cvprw-2021-competition/

The deadline for the final submission is the 11th of June, 2021 (11:59
PM Pacific Time). The winner will be invited to deliver a keynote at the
CVPR workshop on event-based vision:
https://tub-rip.github.io/eventvision2021/

Stay tuned and good luck!

Mathias Gehrig, Davide Scaramuzza

Topical Collection on Neural Computing for IOT based Intelligent Healthcare Systems in Neural Computing and Applications

]

Topical Collection on Neural Computing for IOT based Intelligent Healthcare Systems

(https://www.springer.com/journal/521/updates/17961480)

Scope

Internet of things (IoT) is a dynamic network of sensors, cloud storage and multiple embedded electronic devices connected with each other through network connectivity for exchange of data. IoT is bringing paradigm shift in field of intelligent systems as multiple connected device makes the system more robust.

The real time health monitoring by sophisticated sensors will not only improve life style of patients, but can also emerge as life saver in critical situations. The data from IoT sensors will be stored in clouds which will be then analysed and shared with healthcare professionals. The healthcare professional can diagnose the condition and provide online consultation. In this manner telemedicine is able to provide healthcare services to vast population in a cost effective manner. Therefore, there is a huge potential in research in field of IoT in healthcare domain.

Neural Computing has come a long a way since its conception. Deep learning is quite a popular variant of Neural Computing that is in high demand these days. Deep learning is a large architecture comprising of a multilayer artificial neural network (ANN). The structure is inspired by data processing capability of human brain and imitates the way a human being would learn new things. Deep learning finds its application in various fields like image/speech recognition, natural language processing, etc. Deep learning is emerging as a best contender for healthcare data analysis; it has immense opportunity in research.

With the advent of Internet of Things (IoT) pioneering work is done in patient health monitoring. IoT is also behind emergence of wearable medical devices. The data from wearable devices or other devices could help in conquering ailments at very early stage.  Enormous amount of data is generated from different medical instruments including wearable medical devices. The amount of data generated is beyond the capability of humans to monitor. In this manner medical field is looking towards technologies for data analysis.

Medical science and technology are coming together to provide better healthcare services. Deep learning has emerged as number one contender for medical data analysis. The generated medical data includes various modalities in form of single dimension signals like ECG, EEG, and multidimensional signals in form of MRI, CT, angiography, X-ray images, etc. Deep learning has been proved a phenomenal tool in the field of image processing and data analysis. Application of deep learning in healthcare will help in fast track solution to chronic ailments.

The intermingling disciplines of healthcare data analysis, deep learning and IoT are pathway towards better and smart healthcare services, it is the high time for research in this area, this field have attracted researchers in past and will continue to do so. The research covers numerous multidisciplinary topics catering towards academia and industries.

Recommended topics include (but are not limited to) the following:

  • IoT sensors for smart health devices
  • Data security in IoT based healthcare
  • Telemedicine and medical informatics
  • Medical image classification with deep learning
  • Medical image segmentation with deep learning
  • Medical image fusion with deep learning
  • ECG and EEG classification using learning

Schedule

Deadline for first submission:  Extended 30th September

 

Guest Editors

Dr. Deepak Kumar Jain (Lead Guest Editor),  Chongqing University of Posts and Telecommunications, China, deepak@cqupt.edu.cn
Prof. Thierry Bouwmans, University La Rochelle, France, tbouwman@univ-lr.fr
Prof. Marco Leo, National Research council DHITECH – University Campus of Lecce, via Monteroni , 73100 Lecce, Italy, m.leo@isasi.cnr.it

Design by 2b Consult