Description

Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields.

Key features:

  • Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications.
  • Places an emphasis on tensor and statistical based approaches within object detection and recognition.
  • Provides an overview of image clustering and classification methods which includes subspace and kernel based processing, mean shift and Kalman filter, neural networks, and k-means methods.
  • Contains numerous case study examples of mainly automotive applications.
  • Includes a companion website hosting full C++ implementation, of topics presented in the book as a software library, and an accompanying manual to the software platform.

Object Detection and Recognition in Digital Images: Theory and Practice

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Hardback by Boguslaw Cyganek

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Short Description:

Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a... Read more

    Publisher: John Wiley & Sons Inc
    Publication Date: 23/07/2013
    ISBN13: 9780470976371, 978-0470976371
    ISBN10: 0470976373

    Number of Pages: 560

    Non Fiction , Technology, Engineering & Agriculture , Education

    Description

    Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields.

    Key features:

    • Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications.
    • Places an emphasis on tensor and statistical based approaches within object detection and recognition.
    • Provides an overview of image clustering and classification methods which includes subspace and kernel based processing, mean shift and Kalman filter, neural networks, and k-means methods.
    • Contains numerous case study examples of mainly automotive applications.
    • Includes a companion website hosting full C++ implementation, of topics presented in the book as a software library, and an accompanying manual to the software platform.

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