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Publié par | AI Sciences |
Date de parution | 15 août 2021 |
Nombre de lectures | 3 |
EAN13 | 9781956591088 |
Langue | English |
Poids de l'ouvrage | 5 Mo |
Informations légales : prix de location à la page 0,1200€. Cette information est donnée uniquement à titre indicatif conformément à la législation en vigueur.
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ISBN-13: 978-1-7377085-0-6
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Table of Contents
How to Use This Book?
About the Author
Chapter 1: Introduction
1.1. What Is Computer Vision
1.2. Computer Vision Research Areas
1.2.1. Image Classification
1.2.2. Object Identification
1.2.3. Image Clustering
1.2.4. Image Retouching and Modification
1.2.5. Video Classification
1.2.6. Video Summarization
1.2.7. Moving Object Detection
1.3. Applications of Computer Vision
1.3.1. Healthcare Applications
1.3.2. Applications in Agriculture
1.3.3. Applications in Transportation
1.3.4. Applications in Retail Industry
1.3.5. Sports and Media
1.4. Common Python Libraries for Computer Vision
1.4.1. OpenCV
1.4.2. Pillow Library
1.4.3. Matplotlib
1.4.4. Scikit Learn
1.4.5. TensorFlow
1.4.6. Keras
1.4.7. ImageAI
1.5. Installation and Environment Setup
1.5.1. Windows Setup
1.5.2. Mac Setup
1.5.3. Linux Setup
1.5.4. Using Google Colab Cloud Environment
1.6. Writing Your First Program
1.7. Python Syntax
Chapter 2: Python Crash Course
2.1. Python Variables and Data Types
2.3. Python Operators
2.3. Conditional Statements
2.4. Iteration Statements
2.5. Functions
2.6. Objects and Classes
Exercise 2.1
Exercise 2.2
Chapter 3: Basics of Image Processing
3.1. How Do Computers See Images?
3.2. Image Processing with OpenCV
3.2.1. Opening an Image with OpenCV
3.2.2. Saving an Image in a Different Format
3.2.3. Converting an Image to Grayscale
3.2.4. Cropping an Image
3.2.5. Resizing an Image
3.2.6. Applying Filters
3.2.7. Drawing Shapes
3.3. Image Processing with Python Pillow Library
3.3.1. Opening an Image with Pillow
3.3.2. Saving an Image in a Different Format
3.3.3. Changing Image Color
3.3.4. Changing Image Orientation
3.3.5. Cropping an Image
3.3.6. Modifying an Image Area
3.3.7. Resizing an Image
3.3.8. Applying Filters to Images
Exercise 3.1
Exercise 3.2
Chapter 4: Basics of Video Processing
4.1. How Do Computers Process Videos?
4.2. Capturing and Displaying a Video from a Camera
4.3. Saving a Camera Video
4.4. Importing and Displaying Video from a Video File
4.5. Drawing Shapes on Videos
Exercise 4.1
Exercise 4.2
Chapter 5: Face Detection with OpenCV in Python
5.1. OpenCV for Face Detection
5.2. Installing Library and Importing Images
5.3. Detecting Whole Faces
5.4. Detecting Eyes
5.5. Detecting Smile
5.6. Face Detection from Live Videos
Exercise 5.1
Exercise 5.2
Chapter 6: Introduction to Machine Learning for Computer Vision
6.1. Solving Regression Problems in Machine Learning
6.1.1. Importing and Analyzing the Dataset
6.1.2. Dividing Data into Features and Labels
6.1.3. Converting Categorical Data to Numbers
6.1.4. Divide the Data into Training and Test Sets
6.1.5. Training the Regression Algorithm
6.1.6. Making Predictions and Evaluating the Algorithm
6.2. Solving Classification Problems in Machine Learning
6.2.1. Importing and Analyzing the Dataset
6.2.2. Dividing the Data into Features and Labels
6.2.3. Converting Categorical Data to Numbers
6.2.4. Divide the Data into Training and Test Sets
6.2.5. Training the Classification Algorithm
6.2.6. Making Predictions and Evaluating the Algorithm
6.3. Machine Learning for Computer Vision – Classifying Handwritten Digits
6.3.1. Importing and Visualizing the Dataset
6.3.2. Extracting Features and Labels from the Digits Dataset
6.3.3. Dividing the Data into Training and Test Sets
6.3.4. Training the Classification Algorithm
6.3.5. Making Predictions and Evaluating the Algorithm
6.3.6. Making Predictions on a Single Image
Exercise 6.1
Exercise 6.2
Chapter 7: Introduction to Deep Learning for Computer Vision
7.1. What Is Deep Learning?
7.2. Image Classification with a Densely Connected Neural Network (DNN)
7.2.1. Feed Forward
7.2.2. Backpropagation
7.2.3. Implementing a DNN for Image Classification with TensorFlow Keras
7.3. Image Classification with a Convolutional Neural Network (DNN)
7.3.1. How Do Computers See Images?
7.3.2. The Convolution Operation
7.3.3. The ReLu Operation
7.3.4. The Pooling Operation
7.3.5. Flattening and Fully Connected Layer
7.3.6. Implementing a CNN With TensorFlow Keras
Exercise 7.1
Exercise 7.2
Chapter 8: Transfer Learning for Computer Vision
8.1. Transfer Learning
8.2. Image Classification with a Custom CNN
8.2.1. Importing and Formating the Dataset
8.2.2. Image Augmentation
8.2.3. Training the CNN Model
8.3. Image Classification Using Pretrained VGG 16 Model
8.3.1. What Is the VGG16 Model?
8.4. Image Classification Using Pretrained RESNET Model
8.4.1. What Is a RESNET Model?
8.4.2. Using RESNET50 Model for Image Classification in TensorFlow
Exercise 8.1
Exercise 8.2
Chapter 9: Object Detection with YOLO
9.1. What Is YOLO Algorithm?
9.2. Detecting Objects in Images with YOLO
9.3. Detecting Objects in Videos with YOLO
9.4. Detecting Objects from WebCam Streams with YOLO
Exercise 9.1
Exercise 9.2
Chapter 10: Introduction to GANS
10.1. What Are GANS?
10.2. Handwritten Digit Generation with GANS