This 2004 book is an accessible and comprehensive introduction to machine vision. It provides all the necessary theoretical tools and shows how they are applied in actual image processing and machine vision systems. A key feature is the inclusion of many programming exercises that give insights into the development of practical image processing algorithms. The authors begin with a review of mathematical principles and go on to discuss key issues in image processing such as the description and characterization of images, edge detection, restoration and feature extraction, segmentation, texture and shape. They also discuss image matching, statistical pattern recognition, clustering, and syntactic pattern recognition. Important applications are described, including optical character recognition and automatic target recognition. Software and data used in the book can be found at www.cambridge.org/9780521830461. A useful reference for practitioners, the book is aimed at graduate students in electrical engineering, computer science and mathematics.

Computer vision has widespread and growing application including robotics, autonomous vehicles, medical imaging and diagnosis, surveillance, video analysis, and even tracking for sports analysis. This book equips the reader with crucial mathematical and algorithmic tools to develop a thorough understanding of the underlying components of any complete computer vision system and to design such systems. These components include identifying local features such as corners or edges in the presence of noise, edge preserving smoothing, connected component labeling, stereopsis, thresholding, clustering, segmentation, and describing and matching both shapes and scenes. The extensive examples include photographs of faces, cartoons, animal footprints, and angiograms, and each chapter concludes with homework exercises and suggested projects. Intended for advanced undergraduate and beginning graduate students, the text will also be of use to practitioners and researchers in a range of applications.

This SpringerBrief explores graphical password systems and examines novel drawing-based methods in terms of security, usability, and human computer-interactions. It provides a systematic approach for recognizing, comparing, and matching sketch-based passwords in the context of modern computing systems. The book offers both a security and usability analysis of the accumulative framework used for incorporating handwriting biometrics and a human computer-interaction performance analysis. The chapters offer new perspectives and experimental results regarding model uniqueness, recognition tolerance, and the human-computer interaction. The results demonstrate that biometrics reduce the equal error rate (EER) by more than 10%, and show that people are capable of accurately reproducing a sketch-based password. Fundamentals of Sketch-based Passwords: A General Framework targets computer scientists and engineers focused on computer security, biometrics, and human factors. Advanced-level students in computer science and electrical engineering will find this material useful as a study guide for their classes.