COMPUTER VISION AND DEEP LEARNING: FROM BASICS TO ADVANCED APPLICATIONS

In the last decade, the world has witnessed a profound
transformation in the field of computer vision, propelled by the
revolutionary advancements in deep learning. What was once a domain
rooted in handcrafted features and rule-based algorithms has now

evolved into a vibrant and dynamic field, capable of achieving human-
level perception in many areas—from facial recognition and

autonomous driving to medical diagnostics and industrial automation.
This book, Computer Vision and Deep Learning: From Basics to
Advanced Applications, is designed to provide a comprehensive,
structured, and up-to-date understanding of the key concepts,
methodologies, and technologies that underpin modern computer vision
systems. Whether you are a student embarking on your journey into
artificial intelligence, a researcher aiming to deepen your expertise, or a
practitioner seeking to apply computer vision in real-world scenarios,
this text serves as a holistic resource that bridges theoretical foundations
with practical implementations.
The book begins by establishing a solid grounding in the
fundamentals of computer vision and image processing, progressing
through essential topics such as traditional filtering, feature extraction,
and segmentation techniques. It then transitions seamlessly into deep
learning, introducing neural networks and convolutional architectures,
along with vital training strategies, evaluation metrics, and optimization
techniques.
Subsequent chapters delve into advanced architectures,
including Vision Transformers, Generative Adversarial Networks
(GANs), and attention mechanisms—highlighting how these
innovations have significantly expanded the scope and capability of
vision systems. Moreover, the book places a strong emphasis on
applications, demonstrating how computer vision is driving innovation
across sectors like healthcare, autonomous vehicles, agriculture,
surveillance, and more.

Recognizing the importance of context, ethics, and forward-
looking insights, the book also explores the historical evolution, societal

impact, and future directions of the field. Case studies, both global and
local, are included to illustrate how academic research and industry
collaboration are shaping the next frontier of intelligent visual systems.
Our goal in writing this book is to create a resource that is not only

informative and technically sound but also engaging and forward-
thinking. Through clear explanations, illustrative examples, and a

progression from foundational principles to emerging trends, we hope
to inspire readers to contribute meaningfully to this rapidly evolving
discipline.
We extend our gratitude to the researchers, developers, and
educators whose pioneering work has made this book possible. May this
volume serve as both a guide and an inspiration to all who seek to
explore the vast and exciting landscape of computer vision and deep
learning.