Building Computer Vision Applications Using Artificial Neural Networks

Section 11.Chapter 1: Prerequisite and Software Installation
1.1.Python and PIP
1.1.1.Installing Python and PIP on Ubuntu
1.1.2.Installing Python and PIP on Mac OS
1.1.3.Installing Python and PIP on CentOS 7
1.1.4.Installing Python and PIP on Windows
1.2.Virtualenv
1.2.1.Setup and activate virtualenv
1.3.Tensorflow1.3.1.Installing Tensorflow
1.4.PyCharm IDE
1.4.1.Installing PyCharm
1.4.2.Configuring PyCharm to use virtualenv
1.5.OpenCV
1.5.1.Installing OpenCV
1.5.2.Installing OpenCV4 with Python bindings
1.6.Additional libraries
1.6.1.SciPy
1.6.2.Matplotlib

Chapter 2: Core Concepts of Image and Video Processing
1.7.Image processing
1.7.1.Image basics
1.7.2.Pixel1.7.3.Pixel color
1.7.3.1.Grayscale
1.7.3.2.Color
1.7.4.Coordinate system
1.7.5.Python and OpenCV code to manipulate images1.7.6.Program: loading, exploring and showing image
1.7.7.Program: OpenCV code to access and manipulate pixels
1.8.Drawing
1.8.1.Drawing a line on an image
1.8.2.Drawing a rectangle on an image
1.8.3.Drawing a circle on an image
1.9.Chapter summary
1.10.
2.Chapter 3: Techniques of Image Processing
2.1.Transformation
2.1.1.Resizing
2.1.2.Translation
2.1.3.Rotation
2.1.4.Flipping
2.1.5.Cropping
2.2.Image arithmetic and bitwise operations
2.2.1.Addition
2.2.2.Subtraction
2.2.3.Bitwise operations2.2.3.1.OR
2.2.3.2.AND
2.2.3.3.NOT
2.2.3.4.XOR
2.3.Masking2.4.Splitting and merging channels
2.5.Smoothing and blurring
2.6.Thresholding
2.7.Gradient and edge detection
2.8.Contours
2.9.Chapter summary

Section 2
3.Chapter 4: Building Artificial Intelligence System For Computer Vision
3.1.Image processing pipeline
3.2.Feature extraction
3.2.1.Color histogram
3.2.2.GLCM
3.2.3.HOG
3.2.4.LBP
3.3.Feature selection
3.3.1.Filter
3.3.2.Wrapper
3.3.3.Embedded
3.3.4.Regularization
3.4.Chapter summary

4.Chapter 5: Artificial Neural Network for Computer Vision
4.1.Introduction to ANN
4.1.1.ANN topology
4.1.2.Hyperparameters
4.1.3.ANN model training using TensorFlow
4.1.4.Model evaluation
4.1.5.Model deployment
4.1.6.Use of trained model
4.2.Introduction to Convolution Neural Network (CNN)
4.2.1.Core concepts of CNN
4.2.2.Creating training set for CNN
4.2.3.Training CNN model using TensorFlow
4.2.4.Inspecting CNN model and evaluating model fitness
4.2.5.Using and deployment of trained model
4.3.Introduction to Recurrent Neural Network (RNN) and long short-term Memory (LSTM)
4.3.1.Core concepts of RNN and LSTM
4.3.2.Creating training set for LSTM
4.3.3.LSTM model training using TensorFlow
4.3.4.Inspecting LSTM model and assessing fitness4.3.5.Deploying LSTM models in practice

Section 3
5.Chapter 6: Practical Example 1- Object Detection in Images
6.Chapter 7: Practical Example 2- Object Tracking in Videos
7.Chapter 8: Practical Example 3- Facial Detection
8.Chapter 9: Industrial Application - Realtime Defect Detection in Industrial Manufacturing