Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python

ISBN-13: 9781484241486 / Angielski / Miękka / 2019 / 169 str.

Himanshu Singh

Opis

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You'll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You'll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you'll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. What You Will Learn Discover image-processing algorithms and their applications using PythonExplore image processing using the OpenCV libraryUse TensorFlow, scikit-learn, NumPy, and other librariesWork with machine learning and deep learning algorithms for image processingApply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software developers interested in image processing and computer vision.

Spis treści

Chapter 1: Installation and Environment Setup

 

Chapter Goal: Making System Ready for Image Processing and Analysis

 

No of pages 20

 

Sub -Topics (Top 2)

 

1.       Installing Jupyter Notebook

 

2.       Installing OpenCV and other Image Analysis dependencies

 

3.       Installing Neural Network Dependencies

 

 

Chapter 2: Introduction to Python and Image Processing

 

Chapter Goal: Introduction to different concepts of Python and Image processing Application on it.

 

No of pages: 50

 

Sub - Topics (Top 2)

 

1.     Essentials of Python     

2.     Terminologies related to Image Analysis

 

 

Chapter 3: Advanced Image Processing using OpenCV

 

Chapter Goal: Understanding Algorithms and their applications using Python

 

No of pages: 100

 

Sub - Topics (Top 2):

 

1.       Operations on Images

 

2.       Image Transformations

 

Chapter 4: Machine Learning Approaches in Image Processing

 

Chapter Goal: Basic Implementation of Machine and Deep Learning Models, which takes care of Image Processing, before applications in real-time scenario

 

No of pages: 100

 

Sub - Topics (Top 2):

 

1.       Image Classification and Segmentation

 

2.       Applying Supervised and Unsupervised Learning approaches on Images using Python

 

Chapter 5: Real Time Use Cases

 

Chapter Goal: Working on 5 projects using Python, applying all the concepts learned in this book

 

No of pages: 100

 

Sub - Topics (Top 5):

 

1.     Facial Detection

2.     Facial Recognition

3.     Hand Gesture Movement Recognition

4.     Self-Driving Cars Conceptualization: Advanced Lane Finding

5.     Self-Driving Cars Conceptualization: Traffic Signs Detection

 

Chapter 6: Appendix A

 

Chapter Goal: Advanced concepts Introduction

 

No of pages: 50

 

Sub - Topics (Top 2):

 

1.     AdaBoost and XGBoost

2.     Pulse Coupled Neural Networks

O autorze

Himanshu Singh has more than five years of experience as a data science professional. Currently he is senior data scientist at Unify Technologies Private Limited. He gives corporate training on data science, ML, and DL. He's also a visiting faculty for analytics at the Narsee Monjee Institute of Management Studies, considered one of the premium management institutes in India. He is founder of Black Feathers Analytics and Rise of Literati Clubs.

Blurb

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. 

The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. 

All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application.

You will:

• Discover image-processing algorithms and their applications using Python
  
• Explore image processing using the OpenCV library
  
• Use TensorFlow, scikit-learn, NumPy, and other libraries
  
• Work with machine learning and deep learning algorithms for image processing
  
• Apply image-processing techniques to five real-time projects