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: Computer Vision Metrics
    sub title: Textbook Edition
  name:
    name part: Krig, Scott.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 637 p. 331 illus., 139 illus. in color. online resource.
  abstract: Based on the successful 2014 book published by Apress, this textbook edition is expanded to provide a comprehensive history and state-of-the-art survey for fundamental computer vision methods and deep learning. With over 800 essential references, as well as chapter-by-chapter learning assignments, both students and researchers can dig deeper into core computer vision topics and deep learning architectures. The survey covers everything from feature descriptors, regional and global feature metrics, feature learning architectures, deep learning, neuroscience of vision, neural networks, and detailed example architectures to illustrate computer vision hardware and software optimization methods. To complement the survey, the textbook includes useful analyses which provide insight into the goals of various methods, why they work, and how they may be optimized. The text delivers an essential survey and a valuable taxonomy, thus providing a key learning tool for students, researchers and engineers, to supplement the many effective hands-on resources and open source projects, such as OpenCV and other imaging and deep learning tools.
  table of contents: Image Capture and Representation -- Image Re-processing -- Global and Regional Features -- Local Feature Design Concepts -- Taxonomy of Feature Description Attributes -- Interest Point Detector and Feature Descriptor Survey -- Ground Truth Data, Content, Metrics, and Analysis -- Vision Pipeline and Optimizations -- Feature Learning Architecture Taxonomy and Neuroscience Background -- Feature Learning and Deep Learning Architecture Survey. .
  note: by Scott Krig.
  subject:
    topic: Computer science
  subject:
    topic: Data mining
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational intelligence
  subject:
    topic: Computer Science
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Signal, Image and Speech Processing
  subject:
    topic: Computational Intelligence
  classification: Q334-342
  classification: TJ210.2-211.495
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319337623
  identifier: http://dx.doi.org/10.1007/978-3-319-33762-3
  record info:
    record creation date: 160916
    record change date: 20180108141221.0
    record identifier: 978-3-319-33762-3