Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Patte
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Beschreibung
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 Recognition3. 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 NetworksEigenschaften
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