For many, many years, humans have been dreaming of attempting to develop models that can resemble human intelligence with great precision. From robotic vision to self-driving technology, the search is still on. One area that stands in the ocean of target capabilities is the machines' ability to “act” in the same manner that we do. That includes visualizing and interpreting the world around and understanding the context of the environment. Decades back, this would be a fantasy, as opposed to now, when developing a reliable model is predominantly a matter of resource, time, and specialty. The credit goes to the massive breakthroughs in artificial intelligence that relies on high computational power whose applications are ingrained into our lives. In this blog, we will briefly cover the definition of computer vision and will share the top 23 computer vision books worth reading to drive your entire pipeline forward.
- What do I need to know about computer vision?
- How does it work?
- Why is it relevant?
- Top 23 computer vision books to read
- Summing up
What do I need to know about computer vision?
Computer vision is an interdisciplinary scientific field that encompasses the identification and processing of various elements in visual inputs, including digital images, videos, and even text. It involves the development and application of algorithms that analyze and extract valuable information from visual data, which goes beyond the mere recognition of objects, masks, or similar components. It encompasses tasks like optical character recognition (OCR) for text extraction and understanding, image generation, and other forms of image processing and analysis. In essence, computer vision revolves around the utilization of algorithms to interpret visual inputs and derive meaningful insights from them, contributing to a broader understanding of visual data. Unlike computers, we, humans, have a head start. We process information using our lenses, optical nerves, and retina, while technology uses cameras, data, and algorithms to perform the same function. That being said, you might still be wondering, what makes computer vision models exceptional? Computer vision models stand out due to their remarkable ability to accurately process complex information at incredible speeds, comparable to human capabilities. However, it's important to note that achieving real-time image processing involves a trade-off. To address this, there are specialized clusters of tasks and algorithms designed specifically for real-time image processing.
How does it work?
Data plays a crucial role in initiating the process of computer vision. The initial step involves running data analyses repeatedly until the model gains the ability to distinguish and recognize the provided visual inputs. For instance, to enable a model to identify a street light, it needs to be exposed to a substantial volume of street light images and related items associated with street lighting. By doing so, the model learns the distinctions and defines categories. Consequently, the model becomes capable of making predictions based on the patterns learned from the training data, gradually developing the ability to recognize objects autonomously.
However, it's worth noting that computer vision extends beyond just models. In certain algorithms, such as detecting contours from a mask, it is not always necessary to have an extensive amount of training data. These algorithms can effectively extract relevant information from the provided inputs without requiring a large dataset to train on.
Why is it relevant?
In this modern age, companies are racing against time to implement scalable vision systems to tackle problems that offer value to their businesses. From product automation, and medical diagnosis to recommender systems in marketing, computer vision helps us live and do business anew.
For instance, deep learning vision systems have been able to provide an accurate, early-stage medical diagnosis of life-threatening diseases, such as cancer. When it comes to self-driving technology, AI-assisted human drivers have taken the lead, with Tesla spearheading the new wave. The takeaway? Computer vision's potential is huge, and the shift toward full AI automation is going at full throttle.
Top 23 computer vision books to read
Computer vision is an exciting field to AI enthusiasts: That explains the abundance of literature available on computer vision fundamentals. One of the proven ways of exploring this subject and learning from real-life experiences is through reading. The rest of this article will review some of the top picks among computer vision books in the market. Hopefully, this section will help you pick your next great computer vision book.
Computer vision: algorithm and applications by Richard Szeliski
The book was originally written for undergraduate students taking a computer science course. Yet, it became an essential resource for a wider audience, including researchers and professional practitioners. Written by computer vision expert Richard Szeliski, the book uncovers the different techniques that are applied in analyzing and interpreting images. This book is a must-read for anyone looking for a comprehensive yet subtle introduction to computer vision. Get the book.
Practical deep learning for cloud, mobile & edge by Siddha Ganju, Meher Kasam, and Anirudh Koul
This one provides a practical approach to deep learning applications for the cloud, mobile, and edge devices. Readers of this book get to learn in detail about how to train, tune and deploy modern computer vision models with Keras, Core ML, and TensorFlow. Practical deep learning for cloud, mobile & edge is a great pick for anyone who wants to learn about applications of deep learning using a hands-on approach. Get the book.
Concise computer vision: an introduction into theory and algorithms by Reinhard Klette
Here is another introductory book for newbies, classroom-focused, containing tested exercises and questionnaires. The book covers mathematical approaches to image mapping and explains the topologic and geometric basics for analyzing image regions and distributions of image values. The book is recommended to anyone beginning the journey into computer vision. Get the book.
Computer vision: principles, algorithms, applications, learning by E.R. Davies
Among highly-rated computer vision publications, this one especially stands out. Davies extensively covers fundamental methodologies in computer vision while at the same time expanding on the theoretical part of it, including algorithmic and practical design limitations.
It is tailored for undergraduate and graduate students, professionals in technical fields, engineers, and researchers. This book is unique and palatable to this group because of its up-to-date approach and exposure to modern applications. Get the book.
Computer vision: models, learning and inference by Simon J.D. Prince
The intent behind Simon J.D Prince's piece is to explain relationships that exist between image data and other aspects of estimation, such as object class and 3D structure. The end result is to make new inferences relating to the world from new image data. If you want to dive into real examples of developing and implementing functional vision systems, then this book is for you. Get the book.
Deep learning for vision systems by Mohamed Elgendy
Written by a seasoned researcher, the book covers topics on deep learning and computer vision, explaining how to make the computer understand what it “sees.” It expands further on image classification techniques and object detection, DeepDream, neural style transfer, transfer learning, and generative adversarial networks. If you want to get into image modification, image generation, and facial recognition, give the publication a chance. Get the book.
Modern computer vision with Pytorch by Yeshwanth Reddy and Kishore Ayyaderava
Yeshwanth Reddy and Kishore Ayyaderava provide learners with a hands-on approach to solving over 50 computer vision problems using PyTorch.lx on real-life datasets. The book specifically targets beginners in Pytorch and intermediate machine learning practitioners seeking to develop a stronger grasp of deep learning and PyTorch. Get the book.
Multiple view geometry in computer vision by Richard Hartley and Andrew Zisserman
Richard Hartley and Andrew Zisserman describe the basics in established traditional and modern multiple-view geometries. Moreover, the book addresses the major geometric principles and explains the ways to define objects in algebraic form for easier computation. Finally, it gives learners an understanding of the computer vision structure within a real-world scene. Get the book.
Learning OpenCV 4 computer vision with Python 3 by Joseph Howse and Joe Minichino
In a nutshell, the book covers:
- Latest in-depth cameras
- 3D tracking
- Augmented reality
- Deep neural networks
Readers get the chance to solve computer vision problems with practical code, as well as learn to develop powerful models using Open CV 4 and Python 3. The book focuses on image processing, object categorization, and 2D/3D tracking. Get the book.
Computer vision metrics: survey, taxonomy, and analysis by Scott Krig
Computer Vision Metrics is a cutting-edge resource with extensive sections on historical feature descriptions and machine vision methods. Detailed taxonomy for local, regional, and global features is also presented. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness, and invariance attributes. If this is what you want to explore further, give it a shot. Get the book.
Programming computer vision with Python by Jan Erik Solem
Published in 2012, the book describes how you can perform basic computer vision tasks using Python. You will learn techniques used in robot navigation, medical image analysis, and other applications, covering topics in image mappings and transforms, texture warping, and panorama creation. Additionally, it teaches you how to compute 3D reconstructions from a few images of the same scene. In short, a great pick for entry-level professionals or researchers, and students. Get the book.
Deep learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Deep learning is considered the most comprehensive book of its kind in the market as of now. Although technical, the authors have broken down everything you need to know and understand to get started. Below are some of the concepts covered:
- Probability theory
- Linear algebra
- Numerical computation
- Machine learning
The book further explains various deep learning techniques practitioners use, including optimization algorithms, deep feedforward networks, and regularization. What makes it a top pick is its thorough coverage of the foundational concepts bulleted above. Get the book.
Computer vision: a modern approach by David A. Forsyth
Forsyth covers computer vision comprehensively and gives enough detail for readers to create useful applications. This classic book is best suited for upper-level undergraduates or graduate readers taking computer science or computer engineering courses: a decent learning curve and expert insights are guaranteed.
Learn computer vision using OpenCV: with deep learning CNNs and RNNs by Sunila Gollapudi
Sunila's book targets readers with a basic understanding of Python and machine learning. It covers computer vision pretty broadly, focusing on the following aspects:
- Image and object detection
- Tracking and motion analysis
The book is a must-read for students, researchers, and enthusiasts wanting to build applications using the OpenCV library with Python. Get the book.
Computer vision: advanced techniques and applications by Steve Holden
Steve Holden offers a deep dive into computer vision, bringing contemporary theories and practical applications of AI and video surveillance to the table. This book is dedicated to students, researchers, and enthusiasts who want to stay up-to-date with rapidly evolving computer vision trends and best practices. Get the book.
Learning OpenCV 3: computer vision in C++ with the OpenCV library by Adrian Kaehler and Gary Bradski
Authored by Adrian Kaehler and the creator of the OpenCV library, Gary Bradski, this book is perfect for developers, academics, roboticists, and even hobbyists. With a particular focus on OpenCV, it covers the essential knowledge needed to develop applications that empower computers to perceive visual data and make decisions based on it. Get the book.
Deep learning for computer vision: expert techniques to train advanced neural networks using TensorFlow and Keras by Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani's book examines deep learning techniques that are used in computer vision tasks. It also covers convolutional neural networks, image segmentation, object detection, and several other topics. As it delivers a comprehensive understanding of the concepts, algorithms, and applications, the book is particularly helpful for machine learning practitioners, data scientists, and researchers. Get the book.
Feature extraction & image processing for computer vision by Mark Nixon and Alberto Aguado
As the title suggests, the book centralizes feature extraction and image processing techniques, providing a complete summary of the methods employed in computer vision applications. It also includes topics such as:
- Shape representation
- Texture analysis
- Edge detection
- Object recognition
Not only is the book a vital source for engineers and developers, but it can also be extremely useful for students and educators as it covers essential notions, techniques, and algorithms in image processing. Get the book.
Computer vision: a reference guide by Katsushi Ikeuchi
As a thorough reference guide, the book is especially useful for practicing computer vision professionals and students. It revolves around a vast variety of computer vision topics such as image processing, motion analysis, and augmented reality. Get the book.
Image processing and analysis: variational, PDE, wavelet, and stochastic methods by Tony Chan and Jianhong Shen
With a detailed emphasis on mathematical methods for image processing and analysis, this book connects modern mathematics with state-of-the-art methodologies in image processing, managing current research coherently and logically. The authors have unified various approaches by highlighting the underlying principles that link them to Fourier and spectral analysis, which has been essential to image processing. Get the book.
Numerical algorithms: methods for computer vision, machine learning, and graphics by Justin Solomon
The book introduces a fresh perspective on numerical analysis, adopting a practical approach through various computational task examples such as data processing, computational photography, and animation. The book aims to teach numerical modeling and algorithmic design, while also offering an understanding of the theoretical concepts necessary to master these skills. Additionally, the book incorporates case studies from both computer science research and practical applications, along with references to relevant literature for each specific subject. Get the book.
Practical python and OpenCV by Adrian Rosebrock
By reading this book, readers will acquire fundamental knowledge in computer vision and OpenCV, progressing towards more advanced subjects such as:
- Face detection
- Object tracking in videos
- Handwriting recognition.
The book is rich in practical examples, code snippets, and comprehensive step-by-step explanations to facilitate understanding and learning. Get the book.
OpenCV essential by Oscar Deniz Suarez, et al.
Designed for C++ developers seeking a rapid introduction to OpenCV and its key techniques, this book carries a working knowledge of computer vision and image processing. Through a series of examples, it offers a comprehensive overview of OpenCV's major modules and algorithms, incorporating the latest features found in version 3.0. Get the book.
Summing up
Consistent refinement of technology projects a future where a multitude of tasks is handled by AI integration. Let's face it; computer vision has exploded onto the technology scene over the last few years, which elevates the interest in the matter. If you want to become a computer vision expert or expand your knowledge of its applications, the books discussed will pave the path for you. What resources do you prefer for further learning? We're looking forward to hearing from you! In the meantime, feel free to contact us should you have questions.