Python Image Processing Cookbook

Over 60 recipes to help you perform complex image processing and computer vision tasks with ease

Explore Keras, scikit-image, open source computer vision (OpenCV), Matplotlib, and a wide range of other Python tools and frameworks to solve real-world image processing problems

Key Features

Discover solutions to complex image processing tasks using Python tools such as scikit-image and Keras
Learn popular concepts such as machine learning, deep learning, and neural networks for image processing
Explore common and not-so-common challenges faced in image processing

Book DescriptionWith the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. Les mer
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Paperback
Legg i
Vår pris: 528,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

Explore Keras, scikit-image, open source computer vision (OpenCV), Matplotlib, and a wide range of other Python tools and frameworks to solve real-world image processing problems

Key Features

Discover solutions to complex image processing tasks using Python tools such as scikit-image and Keras
Learn popular concepts such as machine learning, deep learning, and neural networks for image processing
Explore common and not-so-common challenges faced in image processing

Book DescriptionWith the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. This book provides comprehensive coverage of the relevant tools and algorithms, and guides you through analysis and visualization for image processing.

With the help of over 60 cutting-edge recipes, you'll address common challenges in image processing and learn how to perform complex tasks such as object detection, image segmentation, and image reconstruction using large hybrid datasets. Dedicated sections will also take you through implementing various image enhancement and image restoration techniques, such as cartooning, gradient blending, and sparse dictionary learning. As you advance, you'll get to grips with face morphing and image segmentation techniques. With an emphasis on practical solutions, this book will help you apply deep learning techniques such as transfer learning and fine-tuning to solve real-world problems.

By the end of this book, you'll be proficient in utilizing the capabilities of the Python ecosystem to implement various image processing techniques effectively.

What you will learn

Implement supervised and unsupervised machine learning algorithms for image processing
Use deep neural network models for advanced image processing tasks
Perform image classification, object detection, and face recognition
Apply image segmentation and registration techniques on medical images to assist doctors
Use classical image processing and deep learning methods for image restoration
Implement text detection in images using Tesseract, the optical character recognition (OCR) engine
Understand image enhancement techniques such as gradient blending

Who this book is forThis book is for image processing engineers, computer vision engineers, software developers, machine learning engineers, or anyone who wants to become well-versed with image processing techniques and methods using a recipe-based approach. Although no image processing knowledge is expected, prior Python coding experience is necessary to understand key concepts covered in the book.

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Innholdsfortegnelse

Table of Contents

Image Manipulation and Transformation
Image Enhancement
Image Restoration
Binary Image Processing
Image Registration
Image Segmentation
Image Classification
Object Detection in Images
Face Detection and Recognition

Om forfatteren

Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, working with recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master's degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE Data Mining conferences and journals. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses. He is a regular blogger (sandipanweb) and is a machine learning education enthusiast.