Object Detection and Recognition in Digital Images

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. <p>Key features:</p> <ul> <li>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.</li> <li>Places an emphasis on tensor and statistical based approaches within object detection and recognition.</li> <li>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.</li> <li>Contains numerous case study examples of mainly automotive applications.</li> <li>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.</li> </ul>