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 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.