This comprehensive textbook presents a broad review of both traditional (i.e., conventional) and deep learning aspects of object detection in various adversarial real-world conditions in a clear, insightful, and highly comprehensive style. Beginning with the relation of computer vision and object detection, the text covers the various representation of
objects, applications of object detection, and real-world challenges faced by the research community for object detection task. The book addresses various real-world degradations and artifacts for the object detection task and also highlights the impacts of artifacts in the object detection problems. The book covers various imaging modalities and benchmark datasets mostly adopted by the research community for solving various aspects of object detection tasks. The book also collects together solutions and perspectives proposed by the preeminent researchers in the field, addressing not only the background of visibility enhancement but also techniques proposed in the literature for visibility enhancement of scenes and detection of objects in various representative real-world challenges.
Computer Vision: Object Detection in Adversarial Vision is unique for its diverse content, clear presentation, and overall completeness. It provides a clear, practical, and detailed introduction and advancement of object detection in various representative challenging real-world conditions.
Topics and Features:
• Offers the first truly comprehensive presentation of aspects of the object detection in degraded and nondegraded environment.
• Includes in-depth discussion of various degradation and artifacts, and impact of those artifacts in the real world on solving the object detection problems.
• Gives detailed visual examples of applications of object detection in the real world.
• Presents a detailed description of popular imaging modalities for object detection adopted by researchers.
• Presents the key characteristics of various benchmark datasets in indoor and outdoor environment for solving object detection tasks.
• Surveys the complete field of visibility enhancement of degraded scenes, including conventional methods designed for enhancing the degraded scenes as well as the deep architectures.
• Discusses techniques for detection of objects in real-world applications.
• Contains various hands-on practical examples and a tutorial for solving object detection problems using Python.
• Motivates readers to build vision-based systems for solving object detection problems in degraded and nondegraded real-world challenges.
The book will be of great interest to a broad audience ranging from researchers and practitioners to graduate and postgraduate students involved in computer vision tasks with respect to object detection in degraded and nondegraded real-world vision problems.
Mrinal Kanti Bhowmik earned a Bachelor of Engineering (BE) degree in Computer Science and Engineering from the Tripura Engineering College, Government of Tripura, in 2004, a Master of Technology (M.Tech) degree in Computer Science and Engineering from Tripura University (A Central University), India, in 2007, and a PhD in Engineering from Jadavpur University, Kolkata, India, in 2014. He has also spent the Fall 2022 session as a DST-SERB International Research Experience (SIRE) Scholar with SIRE Fellowship, sponsored by the Science and Engineering Research Board (SERB), Government of India at the NYU Center for Cybersecurity (CCS), Tandon School of Engineering, New York University, New York City. He has successfully completed two Department of Electronics and Information Technology (DeitY) (Now Ministry of Electronics and Information Technology [MeitY])-funded projects, one Department of Biotechnology (DBT)-Twinning project, one Society for Applied Microwave Electronics Engineering and Research (SAMEER)-funded project, one Indian Council of Medical Research (ICMR) project, and one Defence Research and Development Organisation (DRDO) project as the Principal Investigator. He is currently the Principal Investigator of one Department of Biotechnology (DBT)-funded project and Co–Principal Investigator of one Indian Council of Medical Research (ICMR) project in collaboration with All India Institute of Medical Sciences (AIIMS), New Delhi.
Since July 2010, he has served with the Department of Computer Science and Engineering, Tripura University as an Assistant Professor and from 26th March, 2023 he has been serving with Department of Computer Science and Engineering, Tripura University as an Associate Professor. He was awarded the Short Term Indian Council of Medical Research (ICMR), Department of Health Research (DHR) International Fellowship from 2019 to 2020 as a Senior Indian Biomedical Scientist for bilateral cooperation in cross-disciplinary research area (i.e., biomedical diagnostic and inferencing systems). His research team has also designed two datasets for object detection in degraded vision named Extended Tripura University Video Dataset (E-TUVD) and Tripura University Video Dataset at Night time (TU-VDN) dataset for the research community in the proposed domain of object detection. His current research interests are in the fields of computer vision, security and surveillance, medical imaging, and image and video forensics.
