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: Foundations of Computer Vision
    sub title: Computational Geometry, Visual Image Structures and Object Shape Detection
  name:
    name part: Peters, James F.
    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: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVII, 431 p. 354 illus., 301 illus. in color. online resource.
  abstract: This book introduces the fundamentals of computer vision (CV), with a focus on extracting useful information from digital images and videos. Including a wealth of methods used in detecting and classifying image objects and their shapes, it is the first book to apply a trio of tools (computational geometry, topology and algorithms) in solving CV problems, shape tracking in image object recognition and detecting the repetition of shapes in single images and video frames. Computational geometry provides a visualization of topological structures such as neighborhoods of points embedded in images, while image topology supplies us with structures useful in the analysis and classiﬁcation of image regions. Algorithms provide a practical, step-by-step means of viewing image structures. The implementations of CV methods in Matlab and Mathematica, classiﬁcation of chapter problems with the symbols (easily solved) and (challenging) and its extensive glossary of key words, examples and connections with the fabric of CV make the book an invaluable resource for advanced undergraduate and ﬁrst year graduate students in Engineering, Computer Science or Applied Mathematics. It offers insights into the design of CV experiments, inclusion of image processing methods in CV projects, as well as the reconstruction and interpretation of recorded natural scenes.
  table of contents: Basics Leading to Machine Vision -- Working with Pixels -- Visualising Pixel Intensity Distributions -- Linear Filtering -- Edges, Lines, Corners, Gaussian kernel and Voronoï Meshes -- Delaunay Mesh Segmentation -- Video Processing. An Introduction to Real-Time and Oﬄine Video Analysis -- Lowe Keypoints, Maximal Nucleus Clusters, Contours and Shapes -- Postscript. Where Do Shapes ﬁt into the Computer Vision Landscape?.
  note: by James F. Peters.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Image processing
  subject:
    topic: Computational intelligence
  subject:
    topic: Engineering
  subject:
    topic: Computational Intelligence
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Applications of Graph Theory and Complex Networks
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Intelligent Systems Reference Library, 124
  identifier: 9783319524832
  identifier: http://dx.doi.org/10.1007/978-3-319-52483-2
  record info:
    record creation date: 170318
    record change date: 20170318074824.0
    record identifier: 978-3-319-52483-2