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# 11 Amazing Computer Vision Books for Data Scientist

## 11 Amazing Computer Vision Books for Data Scientist

In this post, we will share with you the 11 most recommended books in computer vision. This would be divided in 5 theoretical and 6 practical books. Note: This is not in particular order.

You should note that most of the books that are here contain a lot of theoretical concepts, focusing on the mathematics behind computer vision. If you getting into computer vision it is recommended to get the theoretical knowledge before jumping right into the practical part.

## Top 5 Computer Vision Textbooks

### 1. Computer Vision: A Modern Approach

This book is quite one of the most detailed and popular books any student could read. It might seem daunting but it provides a general overview of the entire computer vision project. It also offers students a sufficient amount of information to be able to build useful applications.

The table of content for this book is as follows:

• Part I. Image Formation
• 1. Radiometry – Measuring Light
• 3. Colour
• Part II. Image Models
• 4. Geometric Image Features
• 5. Analytical Image Features
• 6. An introduction to Probability
• Part III. Early Vision: One Image
• 7. Linear Filters
• 8. Edge Detection
• 9. Filters and Features
• 10. Texture
• Part IV. Early Vision: Multiple Images
• 11. The Geometry of Multiple Views
• 12. Stereopsis
• 13. Affine Structure from Motion
• 14. Projective Structure from Motion
• Part V. Mid-Level Vision
• 15. Segmentation Using Clustering Methods
• 16. Fitting
• 17. Segmentation and Fitting Using Probabilistic Methods
• 18. Tracking
• Part VI. High-Level Vision
• 19. Correspondence and Pose Consistency
• 20. Finding Templates Using Classifiers
• 21. Recognition by Relations Between Templates
• 22. Aspect Graphs
• Part VII. Applications and Topics
• 23. Range Data
• 24. Applications: Finding in Digital Libraries
• 25. Application: Image-Based Rendering

### 2. Multiple view geometry in computer vision

Thanks to both of the authors of this book Richard Hartley and Andrew Zisserman who have provided an all-inclusive theoretical material which is best described in detail. It also explains how these methods are applied and implemented. This book is focused on relevant geometric principles, how objects are represented algebraically, computed and applied.

• 1. Introduction
• PART 0. The Background: Projective Geometry, Transformations, and Estimation
• 2. Projective Geometry and Transformations of 2D
• 3. Projective Geometry and Transformations of 3D
• 4. Estimation – 2D Projective Transformations
• 5. Algorithm Evaluation and Error Analysis
• PART I. Camera Geometry and Single View Geometry
• 6. Camera Models
• 7. Computation of the Camera Matrix P
• 8. More Single View Geometry
• PART II. Two-View Geometry
• 9. Epipolar Geometry and the Fundamental Matrix
• 10. 3D Reconstruction of Cameras and Structure
• 11. Computation of the Fundamental Matrix F
• 12. Structure Computation
• 13. Scene Planes and Homographies
• 14. Affine Epipolar Geometry
• PART III. Three-View Geometry
• 15. The Trifocal Tensor
• 16. Computation of the Trifocal Tensor T
• PART IV. N-View Geometry
• 17. N-Linearities and Multiple View Tensors
• 18. N-View Computational Methods
• 19. Auto-Calibration
• 20. Duality
• 21. Cheirality
• 22. Degenerate Configurations
• PART V. Appendices

### 3. Introductory Techniques for 3‑D Computer

This book is one of the oldest computer vision books focused on 3-dimensional problems. It’s a great starting point for undergraduate students looking to grasp a theoretical and algorithmic knowledge about the fundamental problems encountered within computer vision.

• 1. Introduction
• 2. Digital snapshots
• 3. Dealing with Image Noise
• 4. Image Features
• 5. More Image Features
• 6. Camera Calibration
• 7. Stereopsis
• 8. Motion
• 9. Shape from Single-image Cues
• 10. Recognition
• 11. Locating Objects in Space
• A. Appendix

### 4. Computer Vision: Algorithms and Applications

This book provides a collection of computer vision techniques that are mostly used when analyzing and interpreting images. This could be used and applied in different areas such as medical, autonomous vehicles, agriculture e.t.c.

• Book on Amazon: https://amzn.to/332fZCE

• 1. Introduction
• 2. Image formation
• 3. Image processing
• 3. Feature detection and matching
• 5. Segmentation
• 6. Feature-based alignment
• 7. Structure from motion
• 8. Dense motion estimation
• 9. Image stitching
• 10. Computational photography
• 11. Stereo correspondence
• 12. 3D reconstruction
• 13. Image-based rendering
• 14. Recognition

### 5. Computer Vision: Models, Learning, and Inference

This book is a great introduction for advanced undergraduate and graduate students and also includes a broader range of computer vision techniques, probability, and model fitting.

• Book on Amazon:

• 1. Introduction
• 2. Introduction to probability
• 3. Common probability distributions
• 4. Fitting probability models
• 5. The normal distribution
• 6. Learning and inference in vision
• 7. Modeling complex data densities
• 8. Regression models
• 9. Classification models
• 10. Graphical models
• 11. Models for chains and trees
• 12. Models for grids
• 13. Image preprocessing and feature extraction
• 14. The pinhole camera
• 15. Models for transformations
• 16. Multiple cameras
• 17. Models for shape
• 18. Models for style and identity
• 19. Temporal models
• 20. Models for visual words

## Top 6 Computer Vision Programmer Books

### 1. Learning OpenCV: Computer Vision with the OpenCV Library

The book is a great introduction into computer vision on how to get started in building an application that allows computers to visually see, interpret and make decision-based on the seen data. Its a great book for any developer or hobbyist to use the framework quickly. From getting input from cameras, transforming images, segmentation, pattern recognition, tracking stereo vision and machine learning algorithms.

• Book on Amazon:

• 1. Overview
• 2. Introduction to OpenCV
• 3. Getting to Know OpenCV Data Types
• 4. Images and Large Array Types
• 5. Array Operations
• 6. Drawing and Annotating
• 7. Functions in OpenCV
• 8. Image, Video and Data Files
• 9. Cross-Platform and Native Windows
• 10. Filters and Convolutions
• 11. General Image Transforms
• 12. Image Analysis
• 13. Histograms and Templates
• 14. Contours
• 15. Background Subtraction
• 16. Key-points and Descriptors
• 17. Tracking
• 18. Camera Models and Calibration
• 19. Projection and Three-Dimensional Vision
• 20. The Basics of Machine Learning in OpenCV
• 21. Stat-model: The Standard Model for Learning in OpenCV
• 22. Object Detection
• 23. Future of OpenCV

### 2. Practical Computer Vision with Simple-CV

This book covers a good introduction on how to get your hands in building computer vision applications quickly. It introduces you to the basic on CV techniques related to digital image processing, tracking and programming experience is also optional.

• Book on Amazon:

• 1. Introduction
• 2. Getting to Know the SimpleCV Framework
• 3. Image Sources
• 4. Pixels and Images
• 5. The Impact of Light
• 6. Image Arithmetic
• 7. Drawing on Images
• 8. Basic Feature Detection
• 9. Feature Set Manipulation

### 3. Programming Computer Vision with Python

This book explains computer vision in a more broad and practical way that wouldn’t bore you down with a lot of theoretical concepts. Its a great book for students, researchers, and enthusiasts with basic programming and standard mathematical skills that want to get started in building real-world applications. Techniques such as 3D reconstruction, stereo image, and other computer vision applications are written and clearly explained in python.

• Book on Amazon:

• 1. Basic Image Handling and Processing
• 2. Local Image Descriptors
• 3. Image to Image Mappings
• 4. Camera Models and Augmented Reality
• 5. Multiple View Geometry
• 6. Clustering Images
• 7. Searching Images
• 8. Classifying Image Content
• 9. Image Segmentation
• 10. OpenCV

### 4. Deep Learning for Computer Vision

This book is directed towards developers with applied knowledge in machine learning and also on deep learning. It’s a great start if you want to use deep learning for visual recognition on your research, project in making things much quickly and accurately without a lot of experience in this field.

• 1. Introduction to Computer Vision.
• 2. Image Data Preparation.
• 3. Convolutions and Pooling.
• 4. Convolution al Neural Networks.
• 5. Image Classification.
• 6. Object Detection.
• 7. Face Recognition.
• 8. Appendix.

### 5. Deep Learning for Computer Vision

This book is for developers, researchers, and students who have at least some programming experience and want to become proficient in deep learning for computer vision & visual recognition.

• 1. Introduction to Deep Learning.
• 2. What is Deep Learning?
• 3. Image Fundamentals.
• 4. Image Classification Basics.
• 5. Data-sets for Image Classification.
• 6. Configuring Your Development Environment.
• 7. Image Classifier.
• 8. Parameterized Learning.
• 9. Optimization Methods and Regularization.
• 10. Neural Network Fundamentals.
• 11. Convolution al Neural Networks.
• 12. Training a CNN.
• 14. LeNet: Recognizing Handwritten Digits.
• 15. MiniVGGNet: Going Deeper with CNNs.
• 16. Learning Rate Schedulers.
• 17. Spotting Under-fitting and Over-fitting.
• 18. Checking pointing Models.
• 19. Visualizing Network Architectures.
• 20. Out-of-the-box CNNs for Classification.
• 21. Breaking Captchas with a CNN.
• 22. Case Study: Smile Detection.

### 6. Deep Learning for Computer Vision

This book are targeted at Data Scientist and Computer Vision Practitioners who wish to Overcome any problem related to Computer Vision apply the concept of Deep Learning. Some prerequisites before getting started are basic knowledge in python and Machine Learning concept.