Advances in Machine Vision: Strategies and Applications (Series in Computer Science)

This book describes recent strategies and applications for extracting useful information from sensor data. For example, the methods presented by Roth and Levine are becoming widely accepted as the "best" way to segment range images, and the neural network methods for alpha numeric character recognition, presented by K. Yamada, are believed to be the best yet presented. An applied system to analyze the images of dental imprints presented by J. Cote, et al is one of several examples of image processing system designer. Important aspects of the automation of processes are presented in a practical way which can provide immediate new capabilities in fields as diverse as biomedical image processing, document processing, industrial automation, understanding human perception, and the defence industries. The book is organized into secitons describing Model Driven Feature Extraction, Data Driven Feature Extraction, Neural Networks, Model Building, and Applications.