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Building computer vision applications using artificial neural networks : with step-by-step Eeamples in OpenCV and TensorFlow with Python /
Apply computer vision and machine learning concepts in developing business and industrial applications using a practical, step-by-step approach. The book comprises four main sections starting with setting up your programming environment and configuring your computer with all the prerequisites to run...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berkeley, CA :
Apress,
2020.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Intro
- Table of Contents
- About the Author
- About the Technical Reviewer
- Acknowledgments
- Introduction
- Chapter 1: Prerequisites and Software Installation
- Python and PIP
- Installing Python and PIP on Ubuntu
- Installing Python and PIP on macOS
- Installing Python and PIP on CentOS 7
- Installing Python and PIP on Windows
- virtualenv
- Installing and Activating virtualenv
- TensorFlow
- Installing TensorFlow
- PyCharm IDE
- Installing PyCharm
- Configuring PyCharm to Use virtualenv
- OpenCV
- Working with OpenCV
- Installing OpenCV4 with Python Bindings
- Additional Libraries
- Installing SciPy
- Installing Matplotlib
- Chapter 2: Core Concepts of Image and Video Processing
- Image Processing
- Image Basics
- Pixels
- Pixel Color
- Grayscale
- Color
- Coordinate Systems
- Python and OpenCV Code to Manipulate Images
- Program: Loading, Exploring, and Showing an Image
- Program: OpenCV Code to Access and Manipulate Pixels
- Drawing
- Drawing a Line on an Image
- Drawing a Rectangle on an Image
- Drawing a Circle on an Image
- Summary
- Chapter 3: Techniques of Image Processing
- Transformation
- Resizing
- Translation
- Rotation
- Flipping
- Cropping
- Image Arithmetic and Bitwise Operations
- Addition
- Subtraction
- Bitwise Operations
- AND
- OR
- NOT
- XOR
- Masking
- Splitting and Merging Channels
- Noise Reduction Using Smoothing and Blurring
- Mean Filtering or Averaging
- Gaussian Filtering
- Median Blurring
- Bilateral Blurring
- Binarization with Thresholding
- Simple Thresholding
- Adaptive Thresholding
- Otsu's Binarization
- Gradients and Edge Detection
- Sobel Derivatives (cv2. Sobel() Function)
- Laplacian Derivatives (cv2. Laplacian() Function)
- Canny Edge Detection
- Contours
- Drawing Contours
- Summary
- Chapter 4: Building a Machine Learning-Based Computer Vision System
- Image Processing Pipeline
- Feature Extraction
- How to Represent Features
- Color Histogram
- How to Calculate a Histogram
- Grayscale Histogram
- RGB Color Histogram
- Histogram Equalizer
- GLCM
- HOGs
- LBP
- Feature Selection
- Filter Method
- Wrapper Method
- Embedded Method
- Model Training
- How to Do Machine Learning
- Supervised Learning
- Unsupervised Learning
- Model Deployment
- Summary
- Chapter 5: Deep Learning and Artificial Neural Networks
- Introduction to Artificial Neural Networks
- Perceptron
- How a Perceptron Learns
- Multilayer Perceptron
- Why MLP?
- What Is Deep Learning?
- Deep Learning or Multilayer Perceptron Architecture
- Activation Functions
- Linear Activation Function
- Sigmoid or Logistic Activation Function
- TanH/Hyperbolic Tangent
- Rectified Linear Unit
- Leaky ReLU
- Scaled Exponential Linear Unit
- Softplus Activation Function
- Softmax
- Feedforward
- Error Function
- Regression Loss Function
- Binary Classification Loss Function
- Multiclass Classification Loss Function