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 concep
Table of ContentsChapter 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
