Data fusion in robotics and machine intelligence
Data fusion in robotics and machine intelligence
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Markov fusion of a pair of noisy images to detect intensity valleys
International Journal of Computer Vision
Probabilistic image sensor fusion
Proceedings of the 1998 conference on Advances in neural information processing systems II
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Sensor and Information Fusion for Improved Vision-Based Vehicle Guidance
IEEE Intelligent Systems
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Maximum-likelihood array processing in non-Gaussian noise with Gaussian mixtures
IEEE Transactions on Signal Processing
Bounds on bearing and symbol estimation with side information
IEEE Transactions on Signal Processing
Design and performance of combination filters for signalrestoration
IEEE Transactions on Signal Processing
Simultaneous detection of lane and pavement boundaries using model-based multisensor fusion
IEEE Transactions on Intelligent Transportation Systems
Robust classification of blurred imagery
IEEE Transactions on Image Processing
Physiologically motivated image fusion for object detection using a pulse coupled neural network
IEEE Transactions on Neural Networks
Non-Gaussian model-based fusion of noisy images in the wavelet domain
Computer Vision and Image Understanding
Non-parametric and region-based image fusion with Bootstrap sampling
Information Fusion
Towards cognitive image fusion
Information Fusion
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Image fusion algorithms attempt to produce a single fused image that is more informative than any of the multiple source images used to produce the fused image. Analytical studies of image fusion performance have been lacking. Such studies can augment existing experimental studies by addressing some aspects that are difficult to study using experimental methods. Here, an estimation theory approach is employed using a mathematical model based on the observation that each different sensor can provide a different quality when viewing a given object in the scene. One sensor may be better for viewing one object and a different sensor may be better for viewing a different object. The model also acknowledges that distortion and noise will enter into the sensor observations. This model allows us to employ known estimation theory techniques to find the best possible fusion performance, measured in terms of the standard estimation theory measure of performance. This performance measure has not yet received attention in the image fusion community. Some interesting results include the demonstration that a particular weighted averaging approach is shown to yield optimum estimation performance for the model we focus on. It is also shown that it is important to employ a priori information that describes which sensor is able to provide a good view of the important objects in the scene. The essential aspects of some frequently employed fusion approaches are studied and the capabilities of these approaches are analyzed and compared to the best fusion algorithms. We hope this study will encourage further analytical studies of image fusion.