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Computer vision models, learning, and inference

Simon J. D. Prince (Simon Jeremy Damion) 1972-

New York Cambridge University Press 2012

Localização: EPELM - Esc. Politécnica-Bib Eng Elet., Mec. e Naval    (004.8 P935c ) e outros locais(Acessar)

  • Título:
    Computer vision models, learning, and inference
  • Autor: Simon J. D. Prince (Simon Jeremy Damion) 1972-
  • Assuntos: Computer vision; VISÃO COMPUTACIONAL; PROCESSAMENTO DE IMAGENS; COMPUTAÇÃO GRÁFICA
  • Notas: Includes bibliographical references (p. 533-566) and index
  • Notas Locais: Exemplar da Biblioteca do IME é uma reimpressão de 2014
  • Descrição: Introduction -- Introduction to probability -- Common probability distributions -- Fitting probability models -- The normal distribution -- Learning and inference in vision -- Modeling complex data densities -- Regression models -- Classification models -- Graphical models -- Models for chains and trees -- Models for grids -- Image preprocessing and feature extraction -- The pinhole camera -- Models for transformations -- Multiple cameras -- Models for shape -- Models for style and identity -- Temporal models -- Models for visual words
    "This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. [bullet] Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry [bullet] A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking [bullet] More than 70 algorithms are described in sufficient detail to implement [bullet] More than 350 full-color illustrations amplify the text [bullet] The treatment is self-contained, including all of the background mathematics [bullet] Additional resources at www.computervisionmodels.com"--
  • Editor: New York Cambridge University Press
  • Data de criação/publicação: 2012
  • Formato: xi, 580 p. ill. (some col.) 26 cm.
  • Idioma: Inglês

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