"Machine Vision and Machine Learning - Algorithm Principles, Framework Applications and Code Implementation" contains a total of 10 chapters. Chapter 1 is an introduction, including related concepts of machine vision, the development, basic tasks, application fields and difficulties of machine vision, and Marr vision theory; Chapter 2 is digital image processing; Chapter 3 is camera imaging; Chapter 4 is camera calibration; Chapter 5 is Shape from X; Chapter 6 is binocular stereo vision; Chapter 7 is structured light three-dimensional vision; Chapter 8 is depth camera, introducing the currently popular Kinect and Intel Knowledge and related applications of depth cameras such as RealSense; Chapter 9 is the basics of machine learning; Chapter 10 is the application of machine learning in the field of machine vision, including the application of machine learning in pattern recognition, image super-resolution reconstruction, image denoising, target tracking, three-dimensional reconstruction, etc. "Machine Vision and Machine Learning - Algorithm Principles, Framework Application and Code Implementation" except Chapter 1 and Chapter 9, all other chapters are equipped with application cases, including the analysis process of the case, experimental settings, experimental data, program code and operation results. The programming implementation of the case uses MATLAB, C++, and Python programming languages, and uses OpenCV functions, MATLAB vision and graphics toolbox, Scikit-Learn machine learning toolkit, and MatConvNet, TensorFlow, and Keras deep learning frameworks. By explaining the background and principles of the case, design ideas, experimental steps, development environment and tools, and experimental results, readers can understand the relevant content based on the case and strengthen the learning of theory and knowledge in practical engineering applications. At the same time, "Machine Vision and Machine Learning - Algorithm Principles, Framework Application and Code Implementation" also has a certain reference role for researchers and engineers engaged in machine vision and machine learning.
Reader comments
This book covers many aspects of machine vision and machine learning and is very comprehensive. There are rich cases and detailed code implementations, which is very friendly to beginners like me. The author's explanations were also very clear, giving me a deeper understanding of these two fields. I feel that this book is not only suitable for learning, but also helpful for practical work. I recommend it!
This book covers many aspects of machine vision and machine learning and is very comprehensive. There are rich cases and detailed code implementations, which is very friendly to beginners like me. The author's explanations were also very clear, giving me a deeper understanding of these two fields. I feel that this book is not only suitable for learning, but also helpful for practical work. I recommend it!