2023 2nd International Conference on Optics and Machine Vision (ICOMV 2023)

Keynote Speakers

Keynote Speakers

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Prof. Ping Lu, Huazhong University of Science and Technology, China

鲁平教授,华中科技大学

Title: Coming soon...

Abstract: Coming soon...

Experience: Central China distinguished Scholar distinguished professor, third-class professor, doctoral supervisor.  Project leader of national Key RESEARCH and development Program.  Senior member of OSA, Senior member of IEEE, President of IEEE Women Engineers Wuhan Branch, senior member of Chinese Optical Society.  He has won the second prize of National Science and Technology Progress award, the second prize of Hubei Province Science and Technology Invention Award, the first prize and second prize of Science and technology award of China Communication Society, the first prize of Science and Technology Innovation Award of China Optical Engineering Society, etc.  He has won the award of "Three Talents", "top Ten Young Teachers" and the title of "Excellent Communist party member".  He has been invited as chairman of international conference sub-committee, member of technical procedure Committee and invited to give special report.  The course "Optical Fiber Optics" was awarded "National Excellent Course" and "National Excellent Resource Sharing Course", and the teaching research results were awarded the first prize of National Teaching Achievement and hubei Provincial Teaching Achievement.  In the past five years, he has published more than 80 papers, applied for more than 30 national invention patents and authorized more than 20.  A number of scientific research achievements through identification.  



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Prof. KANNIMUTHU SUBRAMANIYAMAnna UniversityChennai, India

Title: Analysis of Deep Learning and its roles in Machine Vision

Abstract: 

Digitalization is firmly entrenched in industrial production, with processes becoming increasingly automated as part of the Industrial Internet of Things (IIoT). Various machines and robots perform more routine production tasks in the IIoT, also known as Industry 4.0. Machine vision technology for automated visual inspection is becoming more accessible and capable thanks to artificial intelligence, specifically machine learning via deep learning. Deep learning mimics how the human brain processes visual input, but with the speed and robustness of a computerised system. The technology ensures quality in the manufacturing industry while also controlling production costs and improving customer satisfaction.

Deep-learning technologies and convolutional neural networks (CNNs) from the field of artificial intelligence (AI) are making their way into machine vision to assist image-processing systems in learning and distinguishing between defects, making identification processes more precise. Traditional image processing and analysis are still used to locate regions of interest within images, which speeds up the overall process and makes it more robust.  

CNNs must first be trained before they can be used for deep learning. This training process relates to the object's external features, such as colour, shape, texture, and surface structure. Based on these properties, the objects are classified and allocated more precisely later. A developer must laboriously define and manually verify the individual features in traditional machine vision methods. However, self-learning algorithms are used in deep learning to automatically find and extract unique patterns in order to differentiate between specific classes. In this session, Deep Learning and its roles in Machine Vision are investigated extensively. Real world case studies such as Image classification and Defect detection are discussed in this session. 


Experience: Kannimuthu Subramaniyam is currently working as Professor in the Department of Computer Science and Engineering at Karpagam College of Engineering, Coimbatore, Tamil Nadu, India. He is also an In-Charge for the Center of Excellence in Algorithms. He is an IBM Certified Cybersecurity Analyst. He did PhD in Computer Science and Engineering at Anna University, Chennai. He did his M.E (CSE) and B.Tech (IT) at Anna University, Chennai. He has more than 15 years of teaching and industrial experience. He is the recognized supervisor of Anna University, Chennai. Two PhD candidate is completed their research under his guidance. He is now guiding 7 PhD Research Scholars. He has published 57 research articles in various International Journals. He published 2 books ("Artificial Intelligence" & “LinkedList Demystified-A Placement Perspective” and 3 Book Chapters (WOS / Scopus Indexed). He is acting as mentor / consultant for DeepLearning.AI, Hubino, MaxByte Technologies and Dhanvi Info Tech, Coimbatore. He is the expert member for AICTE Student learning Assessment Project (ASLAP).



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Prof. Bin Liu, Dalian University of Technology, China

刘斌教授,大连理工大学


Title: Coming soon...

Abstract: Coming soon...

Experience: Bin Liu, Professor, PhD supervisor, Head of Digital Media Technology Department, Director and Technical Leader of Liaoning Provincial Key Laboratory of Medical Simulation Technology, High-end talent in Dalian, member of GF project evaluation expert group, CCF member of Chinese Computer Society, ACM member of American Computer Society, MICCAI member. He received his B.E. degree, M.S. degree and Ph.D. degree from Dalian University of Technology in 2000 to 2009, and he conducted a joint research as a visiting scholar at National University of Singapore in 2018-2019. His main research interests are computer vision and graphic images, medical image processing and 3D reconstruction, computer-aided preoperative planning and simulation.