Computer Vision: Principles, Algorithms, Applications, Learning (formerly titled Computer and Machine Vision) offers a clear and systematic presentation of the basic principles of computer vision. This textbook covers both the theoretical foundations and the practical and algorithmic design aspects, suitable for both bachelor and master students, researchers, and R&D engineers.
Description
- Three new chapters on Machine Learning with attention to basic classification, probabilistic models, and in-depth explanation of Deep Learning networks, including a new chapter on Face Detection and Recognition.
- A chapter on Object Segmentation and Shape Models in which machine learning methodologies are applied with practical examples.
- Extensive discussions of, among others, geometric transformations, the EM algorithm, boosting, semantic segmentation, face frontalization, and recurrent neural networks (RNNs).
- Numerous applications and examples, such as detecting biscuits, foreign objects, faces, eyes, lane markings, surveillance systems, vehicles, and pedestrians, demonstrate the practical reality of computer vision in practice.
- The necessary mathematics and theory are made accessible through clear explanations and well-illustrated examples.
- Each chapter contains a section 'Recent Developments' to keep students and professionals up to date in this rapidly changing field.
- Custom-made programming examples, mainly in MATLAB and C++, with methods, assignments, tips, and solutions.
Target Audience and Language
This textbook is in English and suitable for students and professionals in higher education, research, and self-study. The level is tailored to advanced users who want to develop a solid understanding of computer vision.
Reliable Delivery
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