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90.8 MB
Info Hash:
D1FED4EF5B020E73C6E9AE98440DBBEF41A785B4
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Added:
Oct. 14, 2025, 1:29 p.m.
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(Last updated: Oct. 14, 2025, 1:30 p.m.)
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| Li H. Geometry Fundamentals of Computer Vision 2025.pdf | 90.8 MB |
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28.0 MB
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| Uploaded by indexFroggy | Size 28.0 MB | Health [ 27 /1 ] | Added 2023-07-02 |
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NOTE
SOURCE: Li H. Geometry Fundamentals of Computer Vision 2025
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COVER
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MEDIAINFO
Textbook in PDF format This book enables readers to acquire a fundamental knowledge of Computer Vision from the perspective of geometry, including knowledge of image processing and pattern recognition intended for two-dimensional geometry analysis of images, knowledge of Computer Vision intended for three-dimensional geometry analysis of images. From the pedagogic point of view, the author intends that this book helps students develop an ability to flexibly apply geometry fundamentals of Computer Vision to solve practical engineering problems. In this sense, this book is also a professional reference for engineers dedicated to Computer Vision involved intelligent systems. This book attaches importance to clarification of how relevant knowledge of Computer Vision stems from practical applications and emphasizes the dialectic relationship between the knowledge and practical applications, enabling readers not only to “know how” for practice, but also to “know why” in terms of mathematical essence. Throughout this book, the author tends to provide detailed theoretical derivations and explanations to clarify essential reasons behind Computer Vision methods. Besides, this book provides plenty of original demonstration code scripts (in MatLAB) that are complete, interesting, easy for practice, and of application values for engineering activities. By code demonstration, the author presents how to flexibly take advantage of geometry fundamentals of Computer Vision to realize various kinds of visual effects that are actually technology basis of many interesting and useful applications. Computer Vision is nowadays a popular concept, thanks to ubiquitousness of computer vision related applications and public media’s wide broadcasting of them in the society, such as face detection and fingerprint recognition in the context of intelligent offices, lane detection, traffic sign detection, and pedestrian detection in the context of intelligent vehicles, cereal grain inspection in the context of intelligent agriculture, license plate location and human gait analysis in the context of intelligent surveillance, video games and virtual reality in the context of entertainment. Nowadays, the Internet is maturely developed. Most knowledge in typical computer vision references and open-source code such as the famous OpenCV are easily accessible on the Internet. If readers are just interested in having available code to fulfil certain computer vision task and are probably interested as well in acquiring some relevant knowledge explaining the specific code, they can fairly take advantage of the Internet only, without resorting to any book on computer vision. Then what is the motivation of writing this book? Writing a book on Geometry Fundamentals of Computer Vision is motivated directly by the author’s years of teaching activities with his teaching collaborators from Mines ParisTech in the course computer vision at Shanghai Jiao Tong University (SJTU). The course mainly consists of two parts: the part of geometry fundamentals, and the part of machine (especially deep) learning fundamentals. This book focuses right on the former part and aims at providing a systematically organized presentation of geometry fundamentals of Computer Vision for students as well as field engineers. - Attaches importance to clarification of how geometry fundamentals of computer vision stems from practical applications - Emphasizes the dialectic relationship between relevant knowledge and practical applications - Provides plenty of original demonstration code scripts (in MatLAB) that are complete, interesting, and easy for practice. Preface Introduction Image Processing for Feature Extraction Computational Geometry Projective Geometry and Camera Model Camera Calibration Pattern Recognition for Feature Organization Applications Appendix A: Integral Image Appendix B: Illusion of Brightness Appendix C: Image Filtering Appendix D: Curves and Surfaces Appendix E: Scale-Space Theory Appendix F: Equations Appendix G: Artificial Neural Networks
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