This thesis investigates the issues of implementing multi-sensor imaging system for surveillance applications. Three topics for the fusion of infrared and electro-optic images are studied, i.e. registration, fusion, and evaluation. The first topic is the image registration or alignment, which is to associate corresponding pixels in multiple images to the same physical point in the scene. A trajectory-based method for registering infrared and electro-optic video sequences is proposed in this study. The initial registration parameters are derived from matching the trajectories across the consecutive video frames. Further refinement can be carried out by applying a maximum mutual information approach. The frame difference, from which the feature point is detected, is found with an image structural similarity measurement. The second topic is the implementation of pixel-level fusion. Two applications are considered in this study. Motivated by the adaptive enhancement, a modified pixel-level fusion scheme is proposed to implement the context enhancement. A visual image is first enhanced with the corresponding infrared image. Then, the enhanced image is fused with the visual image again to highlight the background features. This achieves a context enhancement most suitable for human perception. As the application of multi-sensor concealed weapon detection (CWD) is concerned, this thesis clarifies the requirements and concepts for CWD. How the CWD application can benefit from multi-sensor fusion is identified and a framework of multi-sensor CWD is proposed. A solution to synthesize a composite image from infrared and visual image is presented with experimental results. The synthesized image, on one hand provides both the information of personal identification and the suspicious region of concealed weapons; on the other hand implements the privacy protection, which appears to be an important aspect of the CWD process. The third topic is about the fusion performance assessment. In this study, the evaluation metrics are developed for reference-based assessment and blind assessment respectively. An absolute measurement of image features, namely phase congruency, is employed. Future work should include the reliability and optimization study of multiple image sensors from applications' and human perception-related perspectives. This thesis is a contribution to such research.

Taking another lesson from nature, the latest advances in image processing technology seek to combine image data from several diverse types of sensors in order to obtain a more accurate view of the scene: very much the same as we rely on our five senses. Multi-Sensor Image Fusion and Its Applications is the first text dedicated to the theory and practice of the registration and fusion of image data, covering such approaches as statistical methods, color-related techniques, model-based methods, and visual information display strategies. After a review of state-of-the-art image fusion techniques, the book provides an overview of fusion algorithms and fusion performance evaluation. The following chapters explore recent progress and practical applications of the proposed techniques to solving problems in such areas as medical diagnosis, surveillance and biometric systems, remote sensing, nondestructive evaluation, blurred image restoration, and image quality assessment. Recognized leaders from industry and academia contribute the chapters, reflecting the latest research trends and providing useful algorithms to aid implementation. Supplying a 28-page full-color insert, Multi-Sensor Image Fusion and Its Applications clearly demonstrates the benefits and possibilities of this revolutionary development. It provides a solid knowledge base for applying these cutting-edge techniques to new challenges and creating future advances.

covers the theoretical foundations of building a biometric multisensor network, which allowsto create a unified urban biometric community provides foundations of the parallel shift technology and the Radon transformation on cellular automata with a hexagonal covering discusses the methods of biometric identification by gait parameters and the geometric shape of the auricle, which are effectively used in a smart city serves as a great resource for undergraduate, graduate students and specialists working and conducting research in the field of biometric information processing, as well as in the development and construction of distributed intelligent systems

Information fusion resulting from multi-source processing, often called multisensor data fusion when sensors are the main sources of information, is a relatively young (less than 20 years) technology domain. It provides techniques and methods for: Integrating data from multiple sources and using the complementarity of this data to derive maximum information about the phenomenon being observed; Analyzing and deriving the meaning of these observations; Selecting the best course of action; and Controlling the actions. Various sensors have been designed to detect some specific phenomena, but not others. Data fusion applications can combine synergically information from many sensors, including data provided by satellites and contextual and encyclopedic knowledge, to provide enhanced ability to detect and recognize anomalies in the environment, compared with conventional means. Data fusion is an integral part of multisensor processing, but it can also be applied to fuse non-sensor information (geopolitical, intelligence, etc.) to provide decision support for a timely and effective situation and threat assessment. One special field of application for data fusion is satellite imagery, which can provide extensive information over a wide area of the electromagnetic spectrum using several types of sensors (Visible, Infra-Red (IR), Thermal IR, Radar, Synthetic Aperture Radar (SAR), Polarimetric SAR (PolSAR), Hyperspectral...). Satellite imagery provides the coverage rate needed to identify and monitor human activities from agricultural practices (land use, crop types identification...) to defence-related surveillance (land/sea target detection and classification). By acquiring remotely sensed imagery over earth regions that land sensors cannot access, valuable information can be gathered for the defence against terrorism. This books deals with the following research areas: Target recognition/classification and tracking; Sensor systems; Image processing; Remote sensing and remote control; Belief functions theory; and Situation assessment.

There is a growing interest in the development and deployment of intelligent surveillance systems in public and private locations. This book consists of a selection of extended versions of presentations made in two symposia on intelligent distributed surveillance systems (IDSS) and brings together the latest developments in the field.