Ebook: Object Detection and Recognition in Digital Images: Theory and Practice

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.

Content:
Chapter 1 Introduction (pages 1–8):
Chapter 2 Tensor Methods in Computer Vision (pages 9–188):
Chapter 3 Classification Methods and Algorithms (pages 189–345):
Chapter 4 Object Detection and Tracking (pages 346–407):
Chapter 5 Object Recognition (pages 408–486):