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001 978-3-319-63360-2
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020 _a9783319633602
_9978-3-319-63360-2
024 7 _a10.1007/978-3-319-63360-2
_2doi
050 4 _aTK5102.9
072 7 _aTJF
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aTJF
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082 0 4 _a621.382
_223
100 1 _aBajcsy, Peter.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_952333
245 1 0 _aWeb Microanalysis of Big Image Data
_h[electronic resource] /
_cby Peter Bajcsy, Joe Chalfoun, Mylene Simon.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXX, 197 p. 103 illus., 93 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _a1 Introduction -- 2 Using Web Image Processing Pipeline for Big Data Microscopy Experiments -- 3 Example Use Cases -- 4 Building Web Image Processing Pipeline for Big Images -- 5 Image Processing Algorithms -- 6 Interoperability Between Software and Hardware -- 7 Supplementary Information.
520 _aThis 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.
650 0 _aSignal processing.
_94052
650 0 _aPattern recognition systems.
_93953
650 0 _aBiomathematics.
_95084
650 1 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aAutomated Pattern Recognition.
_931568
650 2 4 _aMathematical and Computational Biology.
_932033
700 1 _aChalfoun, Joe.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_952334
700 1 _aSimon, Mylene.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_952335
710 2 _aSpringerLink (Online service)
_952336
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319633596
776 0 8 _iPrinted edition:
_z9783319633619
776 0 8 _iPrinted edition:
_z9783319875330
856 4 0 _uhttps://doi.org/10.1007/978-3-319-63360-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c78934
_d78934