Szeliski Computer Vision Book : A Quick Book Review

Are you looking for Szeliski computer vision book? This is one of the best books for computer vision practitioner and experts. This will give a strong grip over computer vision algorithms and their implementation with sample projects. Let’s see the silent features of this amazing book.

Szeliski computer vision book : ( Silent Features ) –

This book will cover the below topics in details with real time code. If you are a beginner in computer vision and looking for a complete roadmap at a place. This book ( Szeliski computer vision book) is the best option for you.

1. Image formation
2. Image processing
3. Feature detection and matching
4. Segmentation
5. Feature-based alignment
6. Structure from motion
7. Dense motion estimation
8. Image stitching
9. Computational photography
10. Stereo correspondence
11. 3D reconstruction
12. Recognition

Usually, Most computer vision books start on deep learning and related models and do not cover the basic parts. But this book will give you an A-Z approach to Computer Vision.

• Best Book for college student and professional because it has organized content as per the syllabus of universities.
• So many mini projects for hands-on knowledge. You will get end chapter exercises that will clear your concepts on Algorithms.
• It will also cover the prerequisites of Linear Algebra, Probability etc in Appendix form. So that you can understand the concepts with full basics.
• Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book
• Here is the supportive website for further studies.

How to Buy this Book?

I personally recommend purchasing this book because it is a benchmark study material in the Computer vision field. You may use this link to purchase this book. Please comment us how did find this book after purchasing it. Your experience can motivate others to learn more from this book.

Thanks 

Data Science Learner Team

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