Computer Vision Methods for Fast Image Classi cation and Retrieval

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The book presents selected methods for accelerating image retrieval and classification in large collections of images using what are referred to as 'hand-crafted features.' It introduces readers to novel rapid image description methods based on local and global features, as well as several techniques for comparing images.
Developing content-based image comparison, retrieval and classification methods that simulate human visual perception is an arduous and complex process. The book's main focus is on the application of these methods in a relational database context. The methods presented are suitable for both general-type and medical images. Offering a valuable textbook for upper-level undergraduate or graduate-level courses on computer science or engineering, as well as a guide for computer vision researchers, the book focuses on techniques that work under real-world large-dataset conditions.

Highlights new research and techniques Presents methods for accelerating image retrieval and classi?cation in large datasets Includes selected methods that are designed to work directly in relational database management systems

Inhalt
Preface.- Chapter 1. Introduction.- Chapter 2. Feature Detection.- Chapter 3. Image Indexing Techniques.- Chapter 4. Novel Methods for Image Description.- Chapter 5. Image Retrieval and Classication in Relational Databases etc.

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09783030121976
    • Auflage 1st edition 2020
    • Sprache Englisch
    • Genre Allgemeines & Lexika
    • Lesemotiv Verstehen
    • Größe H235mm x B155mm x T9mm
    • Jahr 2020
    • EAN 9783030121976
    • Format Kartonierter Einband
    • ISBN 3030121976
    • Veröffentlichung 28.10.2020
    • Titel Computer Vision Methods for Fast Image Classi cation and Retrieval
    • Autor Rafa Scherer
    • Untertitel Studies in Computational Intelligence 821
    • Gewicht 236g
    • Herausgeber Springer International Publishing
    • Anzahl Seiten 148

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