Computer vision is an application-oriented field, where various applications correspond to distinct computer vision tasks. Consequently, this book is organized based on the taxonomy of these tasks: image processing, visual recognition, and scene reconstruction.
Image processing, the foundational step, includes topics like convolution, edge detection, and image segmentation, often referred to as low-level vision tasks. This section covers classical algorithms. Visual recognition, or high-level vision, involves understanding image semantics and includes topics such as image classification, object detection, and action recognition. Deep learning algorithms are highlighted here. Scene reconstruction focuses on recovering 3D structures from images and includes topics like camera calibration and 3D reconstruction, explored through geometry-based algorithms.
Each chapter is presented in Python Notebooks with theoretical explanations, algorithm details, and executable code, allowing readers to focus on text or implementation as needed. Designed for students, educators, researchers, and engineers, this book assumes familiarity with basic mathematics and Python programming.
The translation was done using artificial intelligence. Subsequently, a human revision was done primarily in terms of content.
.- Part I Introduction to Computer Vision
Chapter 1 Exploring Computer Vision
.- Part II Image Processing
Chapter 2 Convolution
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Chapter 3 Image Filtering
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Chapter 4 Template Matching
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Chapter 5 Edge Detection
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Chapter 6 Corner Detection
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Chapter 7 SIFT Feature Detection
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Chapter 8 Image Stitching
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Chapter 9 Image Segmentation
.- Part III Visual Recognition
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Chapter 10 Image Classification
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Chapter 11 Semantic Segmentation
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Chapter 12 Object Detection
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Chapter 13 Instance Segmentation
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Chapter 14 Human Pose Estimation
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Chapter 15 Action Recognition
.- Part IV Scene Reconstruction
Chapter 16 Camera Calibration
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Chapter 17 Motion Field and Optical Flow
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Chapter 18 Parallel Binocular Vision
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Chapter 19 3D Reconstruction.
Wei Shen, Professor at Shanghai Jiao Tong University. He has received the National Natural Science Fund for Excellent Young Scholars. Also, he has served as area chair for top-tier international conferences in the field of computer vision, such as CVPR, ICCV, and NeurIPS. His research interests include computer vision, pattern recognition, deep learning, and the application of these technologies in scenarios like medical diagnostic assistance.
Chongjie Si, PhD student at the Institute of Artificial Intelligence, Shanghai Jiao Tong University, focuses on efficient training of large models and has published several papers in top-tier conferences or journals such as AAAI, KDD, ECCV, and TKDE.
Chen Yang, PhD student at the Institute of Artificial Intelligence, Shanghai Jiao Tong University, was awarded the MICCAI Young Scientist Award in 2023. His research interests are 3D and 4D reconstruction in complex scenarios, and he has published several papers in top-tier conferences or journals such as TOG, CVPR, ICCV, and TMI.
Yong Yu, Professor at Shanghai Jiao Tong University, and the founder of the universitys ACM class as well as the director of the APEX Data and Knowledge Management Laboratory. He has received numerous honors, including National Model Teacher, National Teacher Ethics Role Model, CCF Distinguished Education Award, Shanghai May Day Labor Medal, and Shanghai Jiao Tong University Presidential Award. In 2018, he founded the Boyu Artificial Intelligence Academy, where he innovated the AI curriculum system based on the ACM classs AI program at Shanghai Jiao Tong University, aiming to cultivate outstanding AI algorithm engineers and researchers.
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