VISION TRANSFORMER AND ENGINEERING PERSPECTIVE

The field of image processing has witnessed transformative shifts over
the past decade, evolving from classical algorithmic techniques to the
incorporation of deep learning frameworks. Among these,
Convolutional Neural Networks (CNNs) have long been celebrated for
their unparalleled performance in tasks such as image classification,
object detection, and segmentation. However, as the scale and
complexity of visual data have increased, so too has the demand for
models capable of understanding long-range dependencies, global
context, and diverse modalities. In response to these challenges, Vision
Transformers (ViTs) have emerged as a compelling alternative,
ushering in a new era of architectural innovation in computer vision.
This book, Vision Transformers and CNNs in Image Processing:
Engineering Perspectives, is a comprehensive guide designed to explore
this evolution. It provides readers with a layered understanding of how
CNNs have shaped the landscape of image analysis and how Vision
Transformers are redefining that landscape with their attention-based
mechanisms. The text integrates theoretical concepts, architectural
insights, comparative evaluations, and real-world engineering
applications to offer a well-rounded view of both models.
What makes this book particularly relevant is its engineering-centric
approach. Rather than focusing solely on abstract theory or code
implementations, the chapters contextualize each topic within practical
engineering workflows—highlighting design trade-offs, performance
metrics, hardware considerations, and scalability challenges.
Furthermore, through case studies from both global and Indian contexts,
this work emphasizes the role of vision models in real-world
engineering solutions and national innovation.

Whether you are a student, researcher, industry practitioner, or engineer
seeking to understand the current and future state of image processing,
this book offers the tools and perspectives necessary to navigate and
contribute to this rapidly evolving domain.
We hope this book inspires new ideas, fuels curiosity, and serves as a
foundational text for your journey through the dynamic intersection of
deep learning and visual understanding.