The detection and recognition of objects in images is a key research topic in the computer vision community Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection It is also of interest to graduate students undertaking studies in these areas

Template Matching Techniques in Computer Vision: Theory and Practice

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Template Matching Techniques in Computer Vision

The decision-making processes in an autonomous mechatronic system rely on data coming from multiple sensors. An optimal fusion of information from distributed multiple sensors requires robust fusion approaches. The science of multisensor fusion and integration (MFI) is formed to treat the information merging requirements. MFI aims to provide the system a more accurate perception enabling an optimal decision to be made. The wide application spectrum of MFI in mechatronic systems includes industrial automation, the development of intelligent robots, military applications, biomedical applications, and microelectromechanical systems (MEMS)/nanoelectromechanical systems (NEMS). This paper reviews the theories and approaches of MFI with its applications. Furthermore, sensor fusion methods at different levels, namely, estimation methods, classification methods and inference methods, are the most frequently used algorithms. Future perspectives of MFI deployment are included in the concluding remarks.

Multisensor Fusion and Integration: Theories, Applications, and its Perspectives

The objective of this paper is to review the theories and approaches of multisensor fusion and integration (MFI) with its application in mechatronics. MFI helps the system perceiving changes of the environment and monitoring the system itself. Since each individual sensor has its own inherent defects and limitations, MFI merges the redundant information acquired by multiple sensors synergistically to provide a more accurate perception and make an optimal decision in further. The wide application spectrum of MFI in mechatronics includes the industrial automation, the development of intelligent robots, military applications, biomedical applications, etc. In this paper, the architecture and algorithms of MFI are reviewed, and some implementation examples in industrial automation and robotic applications are presented. Furthermore, sensor fusion methods at different levels, namely, estimation methods, classification methods and inference methods, the most frequently used algorithms in previous researches with their advantages and limitations are summarized. Applications of MFI in robotics and mechatronics are discussed. Future perspectives of MFI deployment are included in the concluding remarks.

Multisensor Fusion and Integration: A Review on Approaches and Its Applications in Mechatronics

Template matching using the parametric template vector with translation, rotation and scale invariance

We propose a template-matching method in which a parametric template space is constructed from a given set of template images then quickly matched to a reference image. In this method geometrical changes in an image object due to translation, rotation or scaling, and non-geometrical changes such as illumination variations or individual variation between objects are represented by template images in the parametric template space. The method also provides a subpixel matching scheme that yields a high-precision estimation of the object position. Experiments using real images have confirmed the effectiveness of the method.

S. Ohara

Papers

A parametric template method and its application to robust matching