Hands-On Image Processing with Python - Softcover

Dey, Sandipan

Softcover

ISBN 10: 1789343739 ISBN 13: 9781789343731

Publisher: Packt Publishing, 2018

Synopsis

Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks.

Key Features

• Practical coverage of every image processing task with popular Python libraries
• Includes topics such as pseudo-coloring, noise smoothing, computing image descriptors
• Covers popular machine learning and deep learning techniques for complex image processing tasks

Book Description

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.

What you will learn

• Perform basic data pre-processing tasks such as image denoising and spatial filtering in Python
• Implement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in Python
• Do morphological image processing and segment images with different algorithms
• Learn techniques to extract features from images and match images
• Write Python code to implement supervised / unsupervised machine learning algorithms for image processing
• Use deep learning models for image classification, segmentation, object detection and style transfer

Who this book is for

This book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.

Table of Contents

1. Getting started with Image Processing
2. Sampling Fourier Transform
3. Convolution and Frequency domain Filtering
4. Image Enhancement
5. Image Enhancement using Derivatives
6. Morphological Image Processing
7. Extracting Image Features and Descriptors
8. Image Segmentation
9. Classical Machine Learning Methods
10. Learning in Image Processing - Image Classification with CNN
11. Object Detection, Deep Segmentation and Transfer Learning
12. Additional Problems in Image Processing

About this title

About the Author

Sandipan Dey is a Data Scientist and Data Science Developer with a wide range of interests in related areas including Computer Vision, Image Processing, Artificial Intelligence, Deep Learning, Natural Language Processing, Distributed Data Mining, Information Retrieval, Algorithms and Mathematics. He has been working on Data Mining, Machine Learning and its application since 2009. Sandipan Dey has been working He was working as a research assistant in the University of Maryland Baltimore County (UMBC), Baltimore (2009-2011) on Data Mining / Distributed Data Mining, from where he has done his Masters in Computer Science in 2011. He has published in a few International Data Mining / Machine Learning Conferences (ICDM'09, NASA-CIDU'10) and Journals (ISSRE'13, IDA'14). He has been working as a Consultant in Advanced Analytics in Wipro (2012-2014) and as a Data Scientist in a few startup companies including ThinkBigAnalytics (in MountainView) and Turnoutnow (current company). He worked on many machine learning projects, POCs, use-cases and a couple of products with real-world data. He has successfully completed many online courses such as - Image Processing courses (Duke, North-Western @Coursera) - Computer Vision courses (UPenn @Coursera, Microsoft @edX) - Computational Photography course (Gatech @Coursera) - Machine Learning (Stanford, Caltech, Columbia, UCSD, UIUC) - Deep Learning (certification from deeplearning.ai by Andrew Ng.) - Probability, Optimization, Statistics (MIT, Harvard, Purdue @edX, JHU @Coursera) - Distributed Machine Learning (UCBerkeley @edX) - R, Python (Columbia, Harvard, Microsoft @edX, JHU, UMich @Coursera) - Artificial Intelligence (Columbia, Microsoft @edX) - Recommender System, Social Network Analysis (Coursera) - NLP, Text Mining (Stanford, Columbia, UIUC @Coursera). He is a regular blogger in his own blog (sandipanweb.wordpress.com) where he writes blogs on Data Science problems. Prior to his masters, he was working as a software developer for around 5 years in a few companies including Microsoft IDC, he has an overall experience of around 15 years. He has done his BE in Computer Science from Jadavpur University, Kolkata.

Edition details

• Publisher: Packt Publishing
• Publication date: 2018
• ISBN 10: 1789343739
• ISBN 13: 9781789343731
• Binding: Paperback
• Language: English
• Number of pages: 492