The main goal of Machine Learning (ML) is generalization; that is, we train an algorithm on a training dataset and we want the algorithm to work with high performance (accuracy) on an unseen dataset. In order to solve a complex image processing task (such as image classification), the more training data we have, we may expect better generalization—ability of the ML model learned, provided we have taken care of overfitting (for example, with regularization). But with traditional ML techniques, not only does it become computationally very expensive with huge training data, but also, the learning (improvement in generalization) often stops at a certain point. Also, the traditional ML algorithms often need lots of domain expertise and human intervention and they are only capable of what they are designed for—nothing more and nothing less. This is where deep learning models are very promising.
Hands-On Image Processing with Python
By :
Hands-On Image Processing with Python
By:
Overview of this book
Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python.
The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing.
By the end of this book, we will have learned to implement various algorithms for efficient image processing.
Table of Contents (20 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Free Chapter
Getting Started with Image Processing
Sampling, Fourier Transform, and Convolution
Convolution and Frequency Domain Filtering
Image Enhancement
Image Enhancement Using Derivatives
Morphological Image Processing
Extracting Image Features and Descriptors
Image Segmentation
Classical Machine Learning Methods in Image Processing
Deep Learning in Image Processing - Image Classification
Deep Learning in Image Processing - Object Detection, and more
Additional Problems in Image Processing
Other Books You May Enjoy
Index
Customer Reviews