3rd Online Computer Vision and Artificial Intelligence Workshop (OnCV&AI)
August 23rd – August 27th, 2021, Tainan, Taiwan
We sincerely invite anyone in the world interested in spatial AI to participate. The workshop is held online and is free for everyone.
Sponsors
Program
Speakers
Live Stream
Recorded Talks
About
Our Sponsors
Time Zone
All times are listed in UTC+8, i.e. China Standard Time (CST) or Taipei Time, which is 8 hours ahead of Coordinated Universal Time (UTC).
Time Zone
All times are listed in UTC+8, i.e. China Standard Time (CST) or Taipei Time, which is 8 hours ahead of Coordinated Universal Time (UTC).
About the Workshop
This workshop is part of a series created for Ph.D. students, and other skilled engineers, to explore online computer vision and artificial intelligence techniques on the OAK-D camera. It consist of a few seminar speeches and a hackathon, distributed over a week.
During the hackathon everything is done online and implemented in real-time by our hackers on our OAK-D cameras as suggested by the audience. Everything is streamed on YouTube and Twitch.
Program
23
August
17:00 – 18:00
Anna Petrovicheva,
CTO at OpenCV.AI
Vision on Edge Devices
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24
August
13:00 – 14:00
Anwaar Ulhaq,
Lecturer and Deputy Leader of Machine Vision and Digital Health Research at Charles Sturt University
The Green Edge of Computer Vision
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25
August
14:00 – 15:00
Jagmohan Meher,
Robotics Engineer/Educator at Skyfi Labs
Computer Vision in Robotics & Teaching OpenCV to Beginners
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15:00 – 16:00
Che-Wei Lin,
Assistant Professor at the National Cheng Kung University
AI-assisted Orthopedics—Development of an Explainable XCuffNet for Rotator Cuff Tear Radiography Classification
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26
August
13:00 – 14:00
Wei-Chun Chiang,
Assistant Professor at the National Cheng Kung University
A food image recognition system for school lunch using deep learning neural network
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14:00 – 15:00
Jagmohan Meher,
Robotics Engineer/Educator at Skyfi Labs
Computer Vision in Robotics & Teaching OpenCV to Beginners
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27
August
10:00 – 12:00
3rd OnCV&AI Hackathon,
National Cheng Kung University
Interactive live exploration of spatial AI applications on the OAK-D camera based on suggestions from the speakers and audience.
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Subscribe to our workshop calendar
Speakers & Hackathon
Bio: Anna is the CTO of OpenCV.AI and a member of the Board in OpenCV.org. She is an expert in Computer Vision and Artificial Intelligence with 11 years of successful experience in the industry. During her career, Anna created solutions for a wide range of industries, including medicine, security, sports, and virtual reality. Anna presented at several conferences worldwide.
Abstract: One of the recent trends in AI is that more and more computations have been moving from the servers to the edge devices – thus making a wide variety of applications possible. In the talk, Anna will talk about how Computer Vision and Deep Learning algorithms are optimized for different edge architectures, and why this move is important.
Anwaar Ulhaq,
Lecturer and Deputy Leader of Machine Vision and Digital Health Research at Charles Sturt University in Australia
The Green Edge of Computer Vision
August 24th, 13:00
Bio: Dr. Anwar Ulhaq is serving as a lecturer and deputy leader, Machine Vision and Digital Health Research in the School of Computing, Mathematics and Engineering, Charles Sturt University. Anwaar holds a PhD (Artificial Intelligence) from Monash University, Australia, professional education in machine learning and AI from Massachusetts institute of technology, USA. He has also worked as a research fellow at the Institute for Sustainable Industries & Liveable Cities, Victoria University, Australia His research interests include signal and image processing, deep learning, data analytics and computer vision. He has published more than 50 peer-reviewed papers in reputed journals and conferences.
Abstract: Edge computing processes information near the edge where it is needed, providing a new approach for drastically conserving energy on a global scale. Growing quality agricultural products by ensuring plant health, monitoring the health of individual trees to increase green areas in our cities, and managing natural disasters such as wildfire prevention are prominent areas where computer vision and edge computing can be integrated for the common good. The essential component of data centres is the opportunity they provide to lower our data-driven society’s energy footprint. This talk will go into how such integration can help us find a green planet.
Jagmohan Meher,
Robotics Engineer/Educator at Skyfi Labs in India
Computer Vision in Robotics & Teaching OpenCV to Beginners
August 25th, 14:00
August 26th, 14:00
Bio: Jagmohan is a robotics engineer and has a passion for teaching. He has conducted numerous workshops and taught students from twelve different countries through online classes. The age of his students vary from 6 to 60 years old. He has a good experience in developing curriculum and pedagogical strategies.
Heading the Research and Development department of Skyfi Labs, he has worked on different technologies like Computer Vision, Robotics, Mechatronics, IoT and Machine Learning. Now, he is a Masters student in the Mechanical Engineering Department at National Cheng Kung University.
Abstract: Learning Computer Vision is easy. Being one of the most trending technologies, lot of students and professionals want to learn and implement it. In this talk, we will begin with the basics of Computer Vision. The best way to learn OpenCV is by building a project. We will build a color based object detector using OpenCV. To do so we will go through hands-on tutorials on basic and advanced image/video operations. While learning from the tutorials, you could practice the code on your system using almost any camera.
Che-Wei Lin,
Assistant Professor at the National Cheng Kung University in Taiwan
AI-assisted Orthopedics—Development of an Explainable XCuffNet for Rotator Cuff Tear Radiography Classification
August 25th, 15:00
Bio: Che-Wei Lin is an assistant professor of Department of Biomedical Engineering, National Cheng Kung University, Taiwan. His research interest includes the AI-based biomedical signal analysis, virtual reality rehabilitation system, the surgical assistive device development, artificial intelligence wearable technology.
Abstract: A deep convolutional neural network, XCuffNet, was developed for rotator cuff tear radiography classification and compared with state-of-the art CNN. The proposed method XCuffNet improved the existing CNN performance and obtained an accuracy of 94.1%. The representative areas in classifying rotator cuff tear radiography are acromion, greater tuberosity, glenoid, and area between acromion and humeral based on feature visualization that correlates with shoulder radiographic features of rotator cuff tear. The XCuffNet exhibits its potential to assist rotator cuff tear radiography classification.
Wei-Chun Chiang,
Assistant Professor at the National Cheng Kung University in Taiwan
A food image recognition system for school lunch using deep learning neural network
August 26th, 13:00
Bio: Wei-Chun Chiang is an project assistant professor of Department of Electrical Engineering, National Cheng Kung University, Taiwan. His research interest includes computer vision, deep learning, digital signal processing, and AIoT system.
Abstract: Nowadays, standard intake of healthy food is necessary for keeping a balanced diet and to avoid obesity. In this topic, we present a system based on deep learning that automatically performs accurate recognition of food images. The proposed recognition method consisting of a sharpness-aware minimization(SAM) optimizer and a convolutional neural network that classifies food into specific categories. Our research team designs this system based on the client-server model. The client is an app that takes and sends food images to the server-side. The server is built by the Django framework and performs image recognition tasks.
We experimented with a total of 41 classes of food categories and more than 25,000 images from school lunch, and the results show that the proposed method achieves 90% recognition accuracy. Our experimental results demonstrate that the proposed system provides suitable accuracy and can serve as a convenient tool for food recognition.
Hackathon
Interactive live exploration of spatial AI applications on the OAK-D camera based on suggestions from the speakers and audience. This is conducted by our hackers: Jose Ramon Chang, Jacob Chen, and Ric Tu, with Rain Wu handling communication with the audience.
Follow the Live Stream
Everything is streamed in English on YouTube and Twitch. Join by clicking on the logo of your preferred service.
Recorded Talks & Hackathon
Anna Petrovicheva
CTO at OpenCV.AI
Vision on Edge Devices
August 23rd,
17:00
Anwaar Ulhaq,
Lecturer and Deputy Leader of Machine Vision and Digital Health Research at Charles Sturt University
The Green Edge of Computer Vision
August 24th,
13:00
Jagmohan Meher,
Robotics Engineer/ Educator at Skyfi Labs
Computer Vision in Robotics & Teaching OpenCV to Beginners
August 25th,
14:00
Online Presentation file (1/2)
Che-Wei Lin,
Assistant Professor at the National Cheng Kung University
AI-assisted Orthopedics—Development of an Explainable XCuffNet for Rotator Cuff Tear Radiography Classification
August 25th,
15:00
Wei-Chun Chiang,
Assistant Professor at the National Cheng Kung University
A food image recognition system for school lunch using deep learning neural network
August 26th,
13:00
Online Presentation file
Jagmohan Meher,
Robotics Engineer/ Educator at Skyfi Labs
Computer Vision in Robotics & Teaching OpenCV to Beginners
August 26th,
14:00
Online Presentation file (2/2)
Hackathon,
Interactive live exploration of spatial AI applications on the OAK-D camera based on suggestions from the speakers and audience.
This is conducted by our hackers: Jose Ramon Chang, Jacob Chen, and Ric Tu, with Rain Wu handling communication with the audience.
August 27th,
10:00
Past & Upcoming Workshops
About the Team
This workshop is arranged by the Nordling Lab located at the Dept. of Mechanical Engineering, National Cheng Kung University. It is sponsored by the National Cheng Kung University in Taiwan. We are part of the “National Cheng Kung University‘s Parkinson’s Disease Quantifiers” team lead by Ass. Prof. Torbjörn Nordling and Asc. Prof. Chi-Lun Lin selected as a finalist in the OpenCV AI competition 2021 out of more than 1400 teams globally.
The team consists of Dr. Akram Ashyani, Jose Chang, Esteban Roman, Tachyon Kuo, Gavin Vivaldy, Jacob Chen, Yushan Lin, Ric Tu, Prof. Chi-Lun Lin, and Prof. Torbjörn Nordling. We are working on quantification of motor degradation in Parkinson’s disease together with Prof. Chun-Hsiang Tan at Kaohsiung Medical University, and Dr. Chad Tsung-Lin Lee and Dr. Chung-Yao Chien at National Cheng Kung University Hospital. More precisely, on analysing the use of micro motions for assessment of motor degradation based on the Unified Parkinson’s Disease Rating Scale (UPDRS).
The National Cheng Kung University‘s Parkinson’s Disease Quantifiers team
About OAK-D
Luxonis and OpenCV AI Kit with Depth (OAK-D) has won the Best Camera and Sensor product of the year award by the Edge AI and Vision Alliance.
Luxonis is the company that has developed the OpenCV AI Kit. We received these cameras as finalists in the OpenCV AI competition 2021.
About NCKU
The National Cheng Kung University (NCKU) is one of the two major comprehensive universities in Taiwan with app. 22 000 full-time students, attracting some of the best students in Taiwan. NCKU is number one in Academia-Industry Collaborations nationwide. Times Higher Education placed NCKU 38th in Impact Rankings 2020, 103rd in Asia University Rankings 2020. Academic Ranking of World Universities placed NCKU 301-400th internationally.
About OpenCV
OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code.
The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 18 million. The library is used extensively in companies, research groups and by governmental bodies.
Along with well-established companies like Google, Yahoo, Microsoft, Intel, IBM, Sony, Honda, Toyota that employ the library, there are many startups such as Applied Minds, VideoSurf, and Zeitera, that make extensive use of OpenCV. OpenCV’s deployed uses span the range from stitching streetview images together, detecting intrusions in surveillance video in Israel, monitoring mine equipment in China, helping robots navigate and pick up objects at Willow Garage, detection of swimming pool drowning accidents in Europe, running interactive art in Spain and New York, checking runways for debris in Turkey, inspecting labels on products in factories around the world on to rapid face detection in Japan.
It has C++, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS. OpenCV leans mostly towards real-time vision applications and takes advantage of MMX and SSE instructions when available. A full-featured CUDAand OpenCL interfaces are being actively developed right now. There are over 500 algorithms and about 10 times as many functions that compose or support those algorithms. OpenCV is written natively in C++ and has a templated interface that works seamlessly with STL containers.
About AIA
was founded in 2020, a leading purpose-driven organization for promoting commercialization of AI application, as well as advanced AI digital transformation for national and global industries.
About Crowdhelix
Crowdhelix is an Open Innovation platform that forges links between an international network of excellent researchers and innovating companies, so that they can plan and deliver pioneering collaborative projects Crowdhelix is open to applications from any organisation, of any size, anywhere in the world, that can demonstrate a strategic commitment to collaborative research and innovation. The network’s main focus is the European Union’s Horizon Europe programme, which has a €95.5 billion budget that funds thousands of collaborative research and innovation projects worldwide. Our goal is to connect leading research institutions and innovative companies around the world, so that together they can plan and deliver pioneering Horizon Europe projects.
About Luxonis
Our mission is to improve the engineering efficiency of embedding performant, spatial AI + CV into products. We do this by building and maintaining the open-source DepthAI ecosystem (more) which is also now the OpenCV AI Kit.
In other words, we build the core technology that allows human-like perception in real products – allowing 0-to-1 applications in nearly every industry. The technology allows solving problems that need human-like perception, but say in a 1″ cube. A good external writeup about Luxonis is on Bloomberg.
We are enabling what was science-fiction as of 2017:
- Wearable devices that perceive the world and allow the blind to perceive through soundscapes
- Embedded systems that can automatically protect endangered species
- Robots that enable organic farming with zero chemicals (by using lasers to target weeds and pests)
- Perception built into heavy machinery to real-time protect the health and safety of workers.
- Perception built into remote areas to autonomously monitor and protect the environment from leaks and other hazardous conditions.
- New forms of communication devices – bridging the gap between the in-person experience and the Zoom experience.
The above are just quick examples. There are countless solutions to fascinating problems being built off of our platform, with 15,000 devices shipped to date, in the hands of thousands of companies, some of which are working to integrate our technology into their own products.
We are a small startup with aims to have an outsized impact on the world. Still in the earliest stages, the solutions built with our technology have been covered in every top news, tech, and science publication in the world (Forbes, WSJ, Bloomberg, Washington Post, Engadget, etc.). We hold the records (independently) for the largest KickStarter raise for a computer vision project, for an AI project, and for a printed circuit board project.
Our mission is to materially improve the engineering efficiency of the world. This is what our team (and backers) previously did in another market (in business WiFi, with UniFi). And we now aim to do the same for this super-power of human-like perception in embedded systems – making it so prototyping with this power takes hours, and implementing it into a product is as fast as an enclosure can be designed and produced.
We are currently hiring:
Register Now!
We sincerely invite anyone in the world interested in spatial AI to participate. The workshop is held online and is free for everyone.
Organizer: The Nordling Lab @ National Cheng Kung University in Taiwan
Honorary Chair: Chair Prof. Cheng-Wen Wu
General Chair: Ass. Prof. Torbjörn Nordling
Technical Chair: Asc. Prof. Chi-Lun Lin
Assisting Chair: Akram Ashyani
Speaker Coordinators: Esteban Roman, Ray Chen
Lead Hacker: Jose Ramon Chang
Assisting Hackers: Jacob Chen, Ric Tu
Chat Host: Rain Wu
Streamer: Austin Su
Camera Operators: Ric Tu, YuShan Lin
Photographer: Gavin Vivaldy
Designer: Victor Osorio
Administrators: Winnie Tu, Anna Chu