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710 2 _aSpringerLink (Online service)
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020 _a9783319633602
024 7 _a10.1007/978-3-319-63360-2
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 _aQA76.9.B45
_b2018 EB
100 1 _aBajcsy, Peter
_eautor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_1http://viaf.org/viaf/2963152140028711100006/
_9673790
245 1 0 _aWeb Microanalysis of Big Image Data
_cby Peter Bajcsy, Joe Chalfoun, Mylene Simon.
264 1 _aCham
_bSpringer International Publishing
_c2018
300 _a1 recurso en línea (XX, 197 páginas)
_b103 ilustraciones, 93 ilustraciones a color
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
490 0 _aEngineering (Springer-11647)
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 3 _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.
988 _aEBSPRINGER_2018
650 7 _2embne
_9495511
_aDatos masivos
650 7 _2embne
_9669495
_aProceso de imágenes
700 1 _aChalfoun, Joe
_eautor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_1http://viaf.org/viaf/196105692/
_9673791
700 1 _aSimon, Mylene
_eautor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_1http://viaf.org/viaf/100256742/
_9673792
776 0 8 _iEdición impresa:
_z9783319633596
776 0 8 _iEdición impresa:
_z9783319633619
776 0 8 _iEdición impresa:
_z9783319875330
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-63360-2
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _b05/2020
_dz
_ek
_zSI