Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Patte
Himanshu Singh
Résumé
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 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
Who This Book Is For
Data scientists and software developers interested in image processing and computer vision.
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
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Apress |
| Auteur(s) | Himanshu Singh |
| Parution | 26/02/2019 |
| Nb. de pages | 169 |
| EAN13 | 9781484241486 |
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