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Mastering Computer Vision with Python: Build Real-World Image Processing, Feature Engineering, and Object Detection Systems
Mastering Computer Vision with Python
A Practical, Hands-On Guide to Image Processing, Feature Engineering, and Real-World Object TrackingUnlock the power of Computer Vision—the technology behind facial recognition, autonomous vehicles, smart surveillance, medical imaging, and immersive AR/VR experiences. Whether you're a beginner stepping into the field or a developer leveling up your skills, this book is your complete roadmap to understanding how machines see the world and how to build your own intelligent visual systems from scratch.
Modern AI may feel complex, but the foundations of computer vision are beautifully logical—and once you learn them properly, everything else begins falling into place. This book takes you through those foundations step by step, using simple explanations, clean code, and real-world examples you can run instantly.
⭐ What You’ll Learn Inside This Book
This isn’t a theory-only book. This is hands-on, practical computer vision taught through real engineering workflows. You’ll learn how
Build a complete computer vision environment using Python, OpenCV, NumPy, and more.
Understand how machines interpret colour, texture, edges, and shapes inside images.
Apply advanced image processing techniques like Gaussian filtering, bilateral smoothing, and gradient extraction.
Master colour spaces such as RGB, HSV, and LAB—and learn when each is the perfect tool.
Segment images using Otsu’s method, adaptive thresholding, and the powerful watershed algorithm.
Extract meaningful features with HOG, LBP, and multi-scale pyramids.
Perform object detection using template matching and similarity measures.
Build real-time object tracking systems with Mean-Shift and CamShift—complete with adaptive region updates.
Work through full, functional projects, not just isolated examples.
This book gives you the confidence and skill to create real applications—not just copy code.
⭐ Why This Book Stands Out
Most computer vision books jump into deep learning immediately, leaving readers confused and overwhelmed. This book does something
It teaches you the core, foundational techniques every CV engineer must know—colour transformations, filtering, segmentation, feature extraction, and object tracking—before you ever need neural networks.
These classical methods are still used in production today because they’
✔ Fast
✔ Reliable
✔ Interpretable
✔ Easy to deploy
✔ Perfect for real-time systems
By mastering these fundamentals first, you set yourself up for real long-term success in computer vision.
⭐ Who This Book Is For
This book is perfect
Students learning computer vision for the first time
Machine learning and data science
A Practical, Hands-On Guide to Image Processing, Feature Engineering, and Real-World Object TrackingUnlock the power of Computer Vision—the technology behind facial recognition, autonomous vehicles, smart surveillance, medical imaging, and immersive AR/VR experiences. Whether you're a beginner stepping into the field or a developer leveling up your skills, this book is your complete roadmap to understanding how machines see the world and how to build your own intelligent visual systems from scratch.
Modern AI may feel complex, but the foundations of computer vision are beautifully logical—and once you learn them properly, everything else begins falling into place. This book takes you through those foundations step by step, using simple explanations, clean code, and real-world examples you can run instantly.
⭐ What You’ll Learn Inside This Book
This isn’t a theory-only book. This is hands-on, practical computer vision taught through real engineering workflows. You’ll learn how
Build a complete computer vision environment using Python, OpenCV, NumPy, and more.
Understand how machines interpret colour, texture, edges, and shapes inside images.
Apply advanced image processing techniques like Gaussian filtering, bilateral smoothing, and gradient extraction.
Master colour spaces such as RGB, HSV, and LAB—and learn when each is the perfect tool.
Segment images using Otsu’s method, adaptive thresholding, and the powerful watershed algorithm.
Extract meaningful features with HOG, LBP, and multi-scale pyramids.
Perform object detection using template matching and similarity measures.
Build real-time object tracking systems with Mean-Shift and CamShift—complete with adaptive region updates.
Work through full, functional projects, not just isolated examples.
This book gives you the confidence and skill to create real applications—not just copy code.
⭐ Why This Book Stands Out
Most computer vision books jump into deep learning immediately, leaving readers confused and overwhelmed. This book does something
It teaches you the core, foundational techniques every CV engineer must know—colour transformations, filtering, segmentation, feature extraction, and object tracking—before you ever need neural networks.
These classical methods are still used in production today because they’
✔ Fast
✔ Reliable
✔ Interpretable
✔ Easy to deploy
✔ Perfect for real-time systems
By mastering these fundamentals first, you set yourself up for real long-term success in computer vision.
⭐ Who This Book Is For
This book is perfect
Students learning computer vision for the first time
Machine learning and data science
349 pages, Kindle Edition
Published December 11, 2025
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