CVRS-MDA-2021: Computer Vision and Recognition Systems Using Machine and Deep Learning Approaches: Fundamentals, technologies and applications |
Website | https://sites.google.com/view/cvrs-mda-editedbook/home |
Submission link | https://easychair.org/conferences/?conf=cvrsmda2021 |
Abstract registration deadline | November 5, 2020 |
Submission deadline | February 24, 2021 |
Scope
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. It seeks to understand and automate tasks that the human visual system can do. To imitate human sight, computer vision must obtain, store, interpret and understand images. It is an artificial intelligence system used in various applications such as convenience stores, driverless car testing, day-to-day medical diagnostics, and crop and livestock safety monitoring. The incredible growth in approaching this milestone was made possible in the iterative learning process through neural networks, machine learning, and deep learning methodologies.
In this edited book, we address one of the critical components for understanding the potential of artificial intelligence, which is to give machines the power of vision and recognition. We plan to present topics devoted to advanced computer vision and recognition methods, technologies and applications which will facilitate ongoing attempts to understand and solve problems in this field. The first part introduces some concepts and fundamentals; the second covers computer vision and recognition methodologies and technologies, and the last part focuses on applications using machine and deep learning algorithms.
Motivation
The motivation for this project stemmed from the fact that there are currently no in-depth books dedicated to the topic of Computer Vision and Recognition Systems Using Machine and Deep Learning Approaches. Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. It seeks to understand and automate tasks that the human visual system can do. To imitate human sight, computer vision must obtain, store, interpret and understand images. It is an artificial intelligence system used in various applications such as convenience stores, driverless car testing, day-to-day medical diagnostics, and crop and livestock safety monitoring. The incredible growth in approaching this milestone was made possible in the iterative learning process through neural networks, machine learning, and deep learning methodologies.
Submission Details
We would appreciate your confirmation of participation by 30 November 2020. Please do not hesitate to contact us if you have any queries. We very much look forward to working with you towards the successful publication of this new book.
Manuscripts that do not follow the formatting rules will be rejected without review. Prospective authors should send their manuscripts electronically through the easychair submission system.
Each chapter should be around 20-25 pages each, and can be submitted as a Word or Latex File. The IET will send you additional information (templates, formatting, permission form, etc.) with the contributor's agreement once you have agreed to contribute to the book. The book is expected to have a total number of 25-30 pages printed pages (based on approximately 550 words per page with a 20% allowance for figures and tables).
We will expect original content for this book with new material and results, not older already or soon to be published papers and chapters. You can of course reuse published material but the percentage of material reuse for the chapter should be less than 30%. The IET will run a piracy software on the full manuscript to control that you are including original material and will reject large amount of material that have already been published so please take this into consideration when writing your chapter.
No Submission or Publication Fees
There are no submission or acceptance fees for manuscripts submitted to this book for publication. All manuscripts are accepted based on a double-blind peer review editorial process.
List of Topics
Part I –Fundamentals and Concepts
- Introduction, Overview and Challenges
- Fundamentals and Concepts: (Computational Models, Evolutionary Algorithms, Machine Learning Algorithms, Deep Learning Algorithms, Neural Networks / CNNs, Image Processing and Synthesis, Machine Intelligence and Pattern Recognition)
- Computer Vision and Recognition-based Safe Automated Systems
- Machine Learning and Deep Learning-based Safe Automated Systems
Part II – Methodologies & Technologies
- A Survey for Exploring Text and Image Features to Classify Images
- Machine Learning and Deep Learning-based Safe Automated Systems Image Indexing Techniques: A SurveyS
- Image-based Modeling and Rendering: A Comprehensive Study
- Reverse Image Search-based Caption Crawler System
- Deep Learning–based Automated Image Searching and Collection Systems
- Intelligent Image Retrieval System using Convolutional Neural Networks
- Multiple Images based automated 3D Face Reconstruction with Deep Neural Networks
- Visual Quality Improvement using Single Image Defogging Technique: Quality Improvement using the Defogging Technique
- A Comprehensive Survey on Eye-tracking of Moving Objects
Part III - Applications
- Applications of Object Detection Techniques using Deep Learning
- Study on the Automatic Lip-Reading System Based on Deep Convolutional Neural Network
- Current Challenges and Applications of DeepFake Detection Systems
- Computer Vision-Based Automatic Target Recognition Systems with Deep Learning Algorithms
- Computer Vision for Semi-automated Mobile Monitoring Applications
- Automated Brain Tumor Robust Semantic Segmentation based on Deep Learning
- A traffic enforcement cameras monitoring to detect the wrong-way movement of vehicles using deep convolutional neural network
- Vehicle Control System Based on Eye, Iris and Gesture Recognition with Eye Tracking
- Deep Learning-based Remote Sensing imagery by Automated Terrain Feature Identification Systems
- Computer vision applications in crop and livestock safety monitoring
- Climate change and Weather monitoring Systems based on deep learning methods
- Computer vision-based water remote sensing monitoring applications
- Conclusion
Editors
- Dr. Chiranji Lal Chowdhary, Associate Professor, School of Information Technology and Engineering, Vellore
- Dr. Mamoun Alazab, Associate Professor, College of Engineering, IT and Environment at Charles Darwin University, Australia
- Dr. Ankit Chaudhary, Assistant Professor, Dept. of Computer Science, the University of Missouri, USA
- Dr. Saqib Hakak, Assistant Professor, Department of Computer Science, University of Northern British Columbia, Canada
- Dr. Thippa Reddy Gadekallu, Associate Professor, School of Information Technology and Engineering, Vellore
Publication
CVRS-MDA-2021 will be published in The Institution of Engineering and Technology (the IET) http://www.theiet.org/.
Contact
All questions about submissions should be emailed to:
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Dr. Chiranji Lal Chowdhary, VIT University, Vellore. (prof.chowdhary@gmail.com)
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Dr. Thippa Reddy G., VIT University, Vellore (thippareddy.g@vit.ac.in)