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024 7 _a10.1007/978-981-99-9799-2
_2doi
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050 4 _aTA1634
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100 1 _aZhu, Hu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aInfrared Small Target Detection
_h[electronic resource] :
_bTheory, Methods, and Algorithms. /
_cby Hu Zhu, Yushan Pan, Lizhen Deng, Guoxia Xu.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXIV, 168 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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505 0 _aChapter 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.
520 _aUncover 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.
650 0 _aImage processing
_xDigital techniques.
650 0 _aComputer vision.
650 0 _aArtificial intelligence.
650 0 _aComputer science
_xMathematics.
650 0 _aComputer science.
650 0 _aSoftware engineering.
650 0 _aProgramming languages (Electronic computers).
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 2 4 _aArtificial Intelligence.
650 2 4 _aMathematics of Computing.
650 2 4 _aTheory of Computation.
650 2 4 _aSoftware Engineering.
650 2 4 _aProgramming Language.
700 1 _aPan, Yushan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aDeng, Lizhen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aXu, Guoxia.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819997985
776 0 8 _iPrinted edition:
_z9789819998005
776 0 8 _iPrinted edition:
_z9789819998012
856 4 0 _uhttps://doi.org/10.1007/978-981-99-9799-2
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