Building computer vision applications using artificial neural networks : with step-by-step Eeamples in OpenCV and TensorFlow with Python

Building computer vision applications using artificial neural networks : with step-by-step Eeamples in OpenCV and TensorFlow with Python / Shamshad Ansari.

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...

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Bibliographic Details
Main Author: Ansari, Shamshad
Format: eBook
Language:English
Published: Berkeley, CA : Apress, 2020.
Subjects:
Computer vision.
Neural networks (Computer science)
Computer vision
Computer programming
Machine learning
Open source software
Python (Computer program language)
Online Access:Click for online access
Table of Contents:
  • 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

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