rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Web Microanalysis of Big Image Data
  name:
    name part: Bajcsy, Peter.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Chalfoun, Joe.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Simon, Mylene.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XX, 197 p. 103 illus., 93 illus. in color. online resource.
  abstract: 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 tip…
  table of contents: 1 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.
  note: by Peter Bajcsy, Joe Chalfoun, Mylene Simon.
  subject:
    topic: Signal processing
  subject:
    topic: Image processing
  subject:
    topic: Speech processing systems
  subject:
    topic: Pattern recognition
  subject:
    topic: Biomathematics
  subject:
    topic: Signal, Image and Speech Processing
  subject:
    topic: Pattern Recognition
  subject:
    topic: Physiological, Cellular and Medical Topics
  classification: TK5102.9
  classification: TA1637-1638
  classification: 621.382
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319633602
  identifier: https://doi.org/10.1007/978-3-319-63360-2
  record info:
    record creation date: 180122
    record change date: 20191026003054.0
    record identifier: 978-3-319-63360-2