In this article, I will be listed out 7 best computer vision books for this year. Computer version has been used in many different areas, like agriculture and retail.
What is computer vision? Computer vision is a field of artificial intelligence that enable computers and systems to derive meaningful information from digital image and videos.
In the aspect of language, computer vision can implement through the python programming language.
Best Computer Vision Books
With the best computer vision books, you will get to understand every little explanation listed above. The computer vision is a field, and the only way to make it easy is to get a book on the field and study it.
In the following, we will provide an up-to-date list of the books we recommend reading to learn about the most popular concepts, algorithms, and applications of AI vision.
7 Best Computer Vision Books to Read so Far
The following are the best computer vision books so far.
Computer Vision: Algorithms and Applications (By Richard Szeliski)
The book offers a comprehensive course in computer vision for undergraduate students in computer science. it is also referred to as “the bible of Computer Vision”.
The book outlines a range of real-world applications and discusses the implementation and practical challenges of computer vision techniques. It is an excellent textbook on modern computer vision and covers all novel methods, except deep learning, which started after the book was published.
The table content include:
- Image formation
- Image processing
- Feature detection and matching
- Feature-based alignment
- Structure from motion
Computer Vision: A Modern Approach
This textbook delivers the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This available presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications.
Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.
The table content is in in part.
The part one is image formation. 2 is image models. 3 early visions: one image till it get to part 7 which is applications and topics.
Multiple View Geometry in Computer Vision
This book is written by Richard Hartley and Andrew Zisserman.
Table content: Introduction
- PART 0. The Background: Projective Geometry, Transformations and Estimation
- PART I. Camera Geometry and Single View Geometry
- PART II. Two-View Geometry
- PART III. Three-View Geometry
- PART IV. N-View Geometry
- PART V. Appendices
This book covers relevant geometric principles and how to represent objects algebraically so they can be computed and applied. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework.
Programming Computer Vision with Python: Tools and algorithms for analyzing images
This book for computer vision is written by Jan Erik Solem and published in 2012. Table content include:
- Basic Image Handling and Processing
- Local Image Descriptors
- Image to Image Mappings
- Camera Models and Augmented Reality
- Multiple View Geometry
- Clustering Images
- Searching Images
This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills. You will learn techniques used in robot navigation, medical image analysis, and other computer vision applications
You will also work with image mappings and transforms, such as texture warping and panorama creation. Compute 3D reconstructions from several images of the same scene and organize images based on similarity or content, using clustering methods.
Practical Computer Vision with SimpleCV – The Simple Way to Make Technology See
This book was written by Kurt DeMaagd, Anthony Oliver, Nathan Oostendorp, and Katherine Scott, and was published in 2012. The Table content include:
- Getting to Know the SimpleCV Framework
- Image Sources
- Pixels and Images
- The Impact of Light
- Image Arithmetic
- Drawing on Images
- Basic Feature Detection
You will learn how to build your own computer vision (CV) applications quickly and easily with SimpleCV, an open-source framework written in Python.
Through examples of real-world applications, this hands-on guide introduces you to basic CV techniques for collecting, processing, and analyzing streaming digital images.
Introductory Techniques for 3-D Computer Vision
This book is an amazing book for computer vision student. Written by Emanuele Trucco and Alessandro Verri and was published in 1998. The Table Content include:
- Digital snapshots
- Dealing with Image Noise
- Image Features
- More Image Features
- Camera Calibration
- Shape from Single-image Cues
This book contains a wide range of fundamental problems encountered within computer vision as you can see the table content. It provides details algorithmic and theoretical solutions for each.
For researchers and scientists in the field of computer vision, electrical and computer engineers. Also suitable for students in advanced studies within these areas.
Multiple View Geometry in Computer Vision
This book is about how to rebuild acts from images using geometry and algebra, with applications for computer vision.
The techniques described in this text range across both classical multiple view geometries as well as modern ones, so it is clear which methods are being used at any given time.
Additionally, it offers a complete introduction to the geometric principles and their algebra representation in terms of camera projection matrices.
What Should I Study For Computer Vision?
One must possess a solid grasp of Machine Learning and Deep Learning concepts. Also, you must learn any programming languages such as Python, C++, C#, etc., along with mathematical concepts such as calculus, linear algebra, etc.
What Is Computer Vision And Example?
Computer vision goes under the names of CV or AI vision. It is a field of artificial intelligence that handles image and video analysis. Computer vision systems include a set of AI techniques.
What Is Computer Vision Types?
Different types of computer vision include image segmentation, object detection, facial recognition, edge detection, pattern detection, image classification, and feature matching.
What Is Deep Learning For Computer Vision?
Deep learning methods can achieve state-of-the-art results on challenging computer vision problems such as image classification, object detection, and face recognition.
How Long Will It Take To Learn Computer Vision?
On average, successful students take 3 months to complete this program. Additionally, you can also practice with the best book for computer vision.