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: High Performance Deformable Image Registration Algorithms for Manycore Processors
  name:
    name part: Shackleford, James
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Kandasamy, Nagarajan
    role:
      role term: author.
  name:
    name part: Sharp, Gregory
    role:
      role term: author.
  name:
    name part: Safari, an O'Reilly Media Company
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xx
    date issued: 2013
    edition: 1st edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (122 pages)
  abstract: High Performance Deformable Image Registration Algorithms for Manycore Processors develops highly data-parallel image registration algorithms suitable for use on modern multi-core architectures, including graphics processing units (GPUs). Focusing on deformable registration, we show how to develop data-parallel versions of the registration algorithm suitable for execution on the GPU. Image registration is the process of aligning two or more images into a common coordinate frame and is a fundamental step to be able to compare or fuse data obtained from different sensor measurements. Extracting useful information from 2D/3D data is essential to realizing key technologies underlying our daily lives. Examples include autonomous vehicles and humanoid robots that can recognize and manipulate objects in cluttered environments using stereo vision and laser sensing and medical imaging to localize and diagnose tumors in internal organs using data captured by CT/MRI scans. Demonstrates how to redesign widely used image registration algorithms so as to best expose the underlying parallelism available in these algorithms Shows how to pose and implement the parallel versions of the algorithms within the single instruction, multiple data (SIMD) model supported by GPUs Provides Programming "tricks" that can help readers develop other image processing algorithms, including registration algorithms for the GPU
  note: Shackleford, James.
  note: Electronic resource.
  note: Electronic reproduction. Boston, MA : Safari, Available via World Wide Web. 2014.
  note: Mode of access: World Wide Web.
  identifier: 9780124078802
  identifier: 9780124077416
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record creation date: 210813
    record change date: 20260130165450.8
    record identifier: in00004136973