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Autor(en): 
  • Scott Krig
  • Computer Vision Metrics: Textbook Edition 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Juni 2018  
    Genre:  EDV / Informatik 
     
    Artificial Intelligence / B / Computational Intelligence / Computer Engineering / computer science / Data Mining / Data Mining and Knowledge Discovery / Digital and Analog Signal Processing
    ISBN:  9783319815954 
    EAN-Code: 
    9783319815954 
    Verlag:  Springer EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 254 mm / B 178 mm / D  
    Gewicht:  1230 gr 
    Seiten:  637 
    Illustration:  XVIII, 637 p. 331 illus., 139 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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.
      



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