Hands-On Image Processing with Python (häftad)
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Häftad (Paperback / softback)
Antal sidor
Packt Publishing Limited
Black & white illustrations
234 x 190 x 27 mm
927 g
Antal komponenter
403:B&W 7.5 x 9.25 in or 235 x 191 mm Perfect Bound on White w/Matte Lam
Hands-On Image Processing with Python (häftad)

Hands-On Image Processing with Python

Expert techniques for advanced image analysis and effective interpretation of image data

Häftad Engelska, 2018-11-30
Skickas inom 10-15 vardagar.
Fri frakt inom Sverige för privatpersoner.
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 DescriptionImage 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 forThis 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.
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Övrig information

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/specializations. He is a regular blogger on his blog (sandipanweb) and is a machine learning education enthusiast.


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