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Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics
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Web Microanalysis of Big Image Data (Paperback, Softcover reprint of the original 1st ed. 2018)
Loot Price: R1,408
Discovery Miles 14 080
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Web Microanalysis of Big Image Data (Paperback, Softcover reprint of the original 1st ed. 2018)
Expected to ship within 18 - 22 working days
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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.
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