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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.
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.
This book provides a complete overview of the state of the art in
color image fusion, the associated evaluation methods, and its
range of applications. It presents a comprehensive overview of
fusion metrics and a comparison of objective metrics and subjective
evaluations. Part I addresses the historical background and basic
concepts. Part II describes image fusion theory. Part III focuses
on quantitative and qualitative evaluation. Part IV presents
several fusion applications, including two primary multiscale
fusion approaches-the image pyramid and wavelet transform-as they
pertain to face matching, biomedical imaging, and night vision.
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