Book : Hands-on Image Processing With Python Expert...

-Titulo Original : Hands-on Image Processing With Python Expert Techniques For Advanced Image Analysis And Effective Interpretation Of Image Data

-Fabricante :

Packt Publishing

-Descripcion Original:

Explore mathematical computations and algorithms for image processing using popular Python tools and frameworks Key Features Gain practical knowledge of every image processing task with popular Python libraries Explore topics such as pseudo-coloring, noise smoothing, and computing image descriptors Cover 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 in social media (face detection), medical imaging (X-rays and CT scans), and security (fingerprint recognition). This book is designed to help you learn the core aspects of image processing, from essential concepts to code using the Python programming language. The book starts by covering classical image processing techniques. Youll then go on to explore the evolution of image processing algorithms, right up to the recent advancements in image processing and computer vision with deep learning. As you progress, youll learn how to use image processing libraries such as PIL, scikit-image, and scipy ndimage in Python. The book will further enable you to write code snippets in Python 3 and implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. Youll gradually be able to implement machine learning models using the Python library, scikit-learn. In addition to this, youll explore deep convolutional neural networks (CNNs), such as VGG-19 with Keras, before progressing to use an end-to-end deep learning model called YOLO for object detection. Later chapters will take you through a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing. By the end of this book, youll have learned how 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 such as Weiner in Python Perform morphological image processing and segment images with different algorithms Get to grips with techniques for extracting features from images and matching images Write Python code to implement supervised machine learning and 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 image processing handbook is for computer vision engineers and machine learning developers who are well-versed in Python programming and want to delve into the various aspects and complexities of image processing. No prior knowledge of image processing techniques is required. Table of Contents Getting started with Image Processing Sampling Fourier Transform 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 Learning in Image Processing - Image Classification with CNN Object Detection, Deep Segmentation and Transfer Learning Additional Problems in Image Processing 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 publishe