04800nam a22006495i 4500 978-981-99-9799-2 DE-He213 20251030111754.0 cr nn 008mamaa 240329s2024 si | s |||| 0|eng d 9789819997992 978-981-99-9799-2 10.1007/978-981-99-9799-2 doi TA1501-1820 TA1634 UYT bicssc COM016000 bisacsh UYT thema Zhu, Hu. author. aut http://id.loc.gov/vocabulary/relators/aut 74361 Infrared Small Target Detection [electronic resource] : Theory, Methods, and Algorithms. / by Hu Zhu, Yushan Pan, Lizhen Deng, Guoxia Xu. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XIV, 168 p. 1 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. 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. Image processing Digital techniques. 64228 Computer vision. Artificial intelligence. 58441 Computer science Mathematics. 64041 Computer science. 58552 Software engineering. 33953 Programming languages (Electronic computers). 66206 Computer Imaging, Vision, Pattern Recognition and Graphics. 65550 Artificial Intelligence. 58441 Mathematics of Computing. 66501 Theory of Computation. 67552 Software Engineering. 33953 Programming Language. 66208 Pan, Yushan. author. aut http://id.loc.gov/vocabulary/relators/aut 74362 Deng, Lizhen. author. aut http://id.loc.gov/vocabulary/relators/aut 74363 Xu, Guoxia. author. aut http://id.loc.gov/vocabulary/relators/aut 74364 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819997985 Printed edition: 9789819998005 Printed edition: 9789819998012 https://doi.org/10.1007/978-981-99-9799-2 ZDB-2-SCS ZDB-2-SXCS 58800 58800