Web microanalysis of big image data /

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Bibliographic Details
Author / Creator:Bajcsy, Peter, author.
Imprint:Cham : Springer, [2018]
©2018
Description:1 online resource : illustrations (some color)
Language:English
Subject:Big data.
Image processing -- Digital techniques.
COMPUTERS -- Image Processing.
Pattern recognition.
Applied mathematics.
Imaging systems & technology.
Big data.
Image processing -- Digital techniques.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11544134
Hidden Bibliographic Details
Other authors / contributors:Chalfoun, Joe, author.
Simon, Mylene, author.
ISBN:9783319633602
3319633600
9783319633596
3319633597
Digital file characteristics:text file PDF
Notes:Includes bibliographical references.
Online resource; title from PDF title page (EBSCO, viewed January 31, 2018).
Summary:This book looks at the increasing interest in running microscopy processing algorithms on big image data by presenting the theoretical and architectural underpinnings of a web image processing pipeline (WIPP). Software-based methods and infrastructure components for processing big data microscopy experiments are presented to demonstrate how information processing of repetitive, laborious and tedious analysis can be automated with a user-friendly system. Interactions of web system components and their impact on computational scalability, provenance information gathering, interactive display, and computing are explained in a top-down presentation of technical details. Web Microanalysis of Big Image Data includes descriptions of WIPP functionalities, use cases, and components of the web software system (web server and client architecture, algorithms, and hardware-software dependencies). The book comes with test image collections and a web software system to increase the reader's understanding and to provide practical tools for conducting big image experiments. By providing educational materials and software tools at the intersection of microscopy image analyses and computational science, graduate students, postdoctoral students, and scientists will benefit from the practical experiences, as well as theoretical insights. Furthermore, the book provides software and test data, empowering students and scientists with tools to make discoveries with higher statistical significance. Once they become familiar with the web image processing components, they can extend and re-purpose the existing software to new types of analyses. Each chapter follows a top-down presentation, starting with a short introduction and a classification of related methods. Next, a description of the specific method used in accompanying software is presented. For several topics, examples of how the specific method is applied to a dataset (parameters, RAM requirements, CPU efficiency) are shown. Some tips are provided as practical suggestions to improve accuracy or computational performance.
Other form:Printed edition: 9783319633596
Standard no.:10.1007/978-3-319-63360-2