Uncover the secrets of cutting-edge research in Infrared Small Target Detection, a crucial resource that delves into the dynamic world of infrared imaging and detection algorithms. This comprehensive book is an indispensable gem for the research community, offering a profound introduction to the theory, methods, and algorithms underlying infrared small object detection. As an invaluable guide, this book explores diverse models and categories of infrared small object detection algorithms, providing meticulous descriptions and comparisons of their strengths and limitations. Perfectly tailored for researchers, practitioners, and students with a passion for infrared imaging and detection, this book equips readers with the necessary knowledge to embark on groundbreaking investigations in this field.

Readers can particularly be drawn to the book's methods, results, and topics, encompassing diverse categories of infrared small object detection algorithms and their corresponding advantages and disadvantages. The book also imparts foundational knowledge in mathematical morphology, tensor decomposition, and deep learning, enabling readers to grasp the underlying principles of these advanced algorithms. Experience the key benefits of Infrared Small Target Detection as readers gain a profound understanding of theory, methods, and algorithms tailored to infrared small object detection. The comprehensive descriptions and comparisons of various algorithm categories empower readers to select the perfect algorithms for their specific applications. Unlock the potential of this groundbreaking resource with a basic understanding of mathematics, statistics, and image processing. Some familiarity with infrared imaging and detection proves advantageous in fully immersing oneself in the wealth of knowledge presented within these pages.

A Comprehensive and In-depth Introduction to Infrared Small Object Detection Provides algorithm Comparison and Selection Guide Presents practical Application-Oriented Approach

Autorentext
Prof Hu Zhu is currently a professor and doctoral supervisor at the School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, affiliated with the Key Laboratory of Image Processing and Transmission in Jiangsu Province. His research primarily focuses on artificial intelligence, image processing, and target perception. He has published and accepted over 70 research papers. He has made significant contributions in the field of target perception, achieving notable results. Dr. Yushan Pan is currently an assistant professor at the School of Advanced Technology, Xi'an Jiaotong-Liverpool University, and a honorable affiliated PhD supervisor at the University of Liverpool. His research focuses on artificial intelligence, computer graphic and image process, and their applications in human-computer supported work, computer supported cooperative Work, and designing interactive technologies. Dr. Pan has published and acceptedover 50 papers in recent years. Dr. Lizhen Deng is currently an associate professor and Master's supervisor at the School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, affiliated with the Key Laboratory of Image Processing and Transmission in Jiangsu Province. Her research focuses on artificial intelligence, image processing, and target perception, with significant contributions in the field of target detection and recognition. In recent years, she has published or had accepted over 50 research papers. Dr. Guoxia Xu is currently a research fellow with School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, affiliated with the Key Laboratory of Image Processing and Transmission in Jiangsu Province. His research focuses on computer vision, image processing, and object. In recent years, he has published over 50 research papers. His research achievements have garnered widespread attention and recognition from the academic community.

Inhalt

Chapter 1: Introduction.- Chapter 2: Preliminaries.-Chapter 3: Morphological Transformation for infrared small object detection.- Chapter 4: Low-rank tensor decomposition for infrared small object detection.- Chapter 5: Deep learning methods for infrared small object detection.- Chapter 6: Performance of different methods. Chapter 7: Summary and Outlook of research on infrared small target detection.