Computational Knowledge Vision: The First Footprints

by Wenbo Zheng, Fei-Yue Wang Ph.D

Overview

Computational Knowledge Vision: The First Footprints presents a novel, advanced framework which combines structuralized knowledge and visual models. In advanced image and visual perception studies, a visual model's understanding and reasoning ability often determines whether it works well in complex scenarios. This book presents state-of-the-art mainstream vision models for visual perception. As computer vision is one of the key gateways to artificial intelligence and a significant component of modern intelligent systems, this book delves into computer vision systems that are highly specialized and very limited in their ability to do visual reasoning and causal inference.Questions naturally arise in this arena, including (1) How can human knowledge be incorporated with visual models? (2) How does human knowledge promote the performance of visual models? To address these problems, this book proposes a new framework for computer vision–computational knowledge vision. - Presents a concept and basic framework of Computational Knowledge Vision that extends the knowledge engineering methodology to the computer vision field - Discusses neural networks, meta-learning, graphs, and Transformer models - Illustrates a basic framework for Computational Knowledge Vision whose essential techniques include structuralized knowledge, knowledge projection, and conditional feedback

Product Details

ISBN-13:	9780443216183
Publisher:	Elsevier Science & Technology Books
Publication date:	08/19/2024
Sold by:	Barnes & Noble
Format:	eBook
Pages:	390
File size:	46 MB
Note:	This product may take a few minutes to download.

About the Author

Wenbo Zheng received his bachelor degree in software engineering from Wuhan University of Technology, Wuhan, China, in 2017. He received his Ph.D. degree in computer science and technology from the Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China, in 2021. He is currently an Associate Professor at the School of Computer and Artificial Intelligence, Wuhan University of Technology. His research interests include computer vision and machine learning.Fei-Yue Wang received his Ph.D. degree in computer and systems engineering from the Rensselaer Polytechnic Institute, Troy, NY, USA, in 1990. He joined The University of Arizona in 1990 and became a Professor and the Director of the Robotics and Automation Laboratory and the Program in Advanced Research for Complex Systems. In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, and in 2002, was appointed as the Director of the Key Laboratory of Complex Systems and Intelligence Science, CAS. In 2011, he became the State Specially Appointed Expert and the Director of the State Key Laboratory for Management and Control of Complex Systems. He is a FELLOW of IEEE, INCOSE, IFAC, ASME, and AAAS. Since 1997, he has been serving as the General or Program Chair of over 30 IEEE, INFORMS, IFAC, ACM, and ASME conferences. He was the President of the IEEE ITS Society from 2005 to 2007, the Vice President of the ACM China Council from 2010 to 2011, and the Vice President and the Secretary-General of the Chinese Association of Automation from 2008-2018. He was the Founding Editor-in-Chief (EiC) of the International Journal of Intelligent Control and Systems from 1995 to 2000, the IEEE ITS Magazine from 2006 to 2007, the IEEE/CAA JOURNAL OF AUTOMATICA SINICA from 2014-2017. He was the EiC of the IEEE Intelligent Systems from 2009-2012, the IEEE TRANSACTIONS ON Intelligent Transportation Systems from 2009-2016, and is the EiC of the IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS from 2017-2020. Currently, he is the President of CAA's Supervision Council, IEEE Council on RFID, and Vice President of IEEE Systems, Man, and Cybernetics Society.

Table of Contents

1. Introduction Computational Knowledge Vision Frameworkn2. Reviewing the Past Enables us to Learnn3. Computational Knowledge Vision Computational Knowledge Vision Solutionn4. Low Vision: Computational Knowledge Vision for Edge Detection Modeln5. Middle Vision: Computational Knowledge Vision for Visual Translation Modeln6. Middle Vision: Computational Knowledge Vision for Jointly Face Recognitionn7. High Vision: Computational Knowledge Vision for Visual Reasoning Model Computational Knowledge Vision Applicationn8. Affective Computing: Computational Knowledge Vision for Depression Detection Modeln9. Medical Computing: Computational Knowledge Vision for COVID-19 Detection Modeln10. Medical Computing: Computational Knowledge Vision for Medical Visual Reasoning Model

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From the Publisher

Presents a novel, advanced framework which combines structuralized knowledge and visual models