An approach to fuse multiple images based on Dempster-Shafer evidential reasoning is proposed in this article. Dempster-Shafer theory provides a complete framework for combining weak evidences from multiple sources. Such situations typically arise in the image fusion problems, where a `real scene' image has to be estimated from incomplete and unreliable observations. By converting images from their spatial domain into the evidential representations, decisions are made to aggregate evidences such that a fused image is generated. The proposed fusion approach is evaluated on a broad set of images and promising results are given.
A feature based approach cooperating with Evolution Strategies for image correspondence estimation is proposed. As an optimization algorithm, Evolution Strategies is employed to search the optimal correspondence through out the source images. Instead of finding the correspondence of the entire image, the spatial relationships ofthe feature configuration in each image are discovered, where feature configuration is defined as the cluster of feature vectors on an image representing homogeneous feature distribution. Employing ES reduces the computational expense and broadens the applicable area since the comparison is restricted to the area inside the search structure of Evolution Strategies, which is defined as an ellipse. The results from various test images prove it to be an efficient and effective approach.
A simulated annealing based coefficient optimization approach to improve the image fusion performance is proposed in this article. This article tries to answer two questions. Firstly, reference images used in most previous literature to measure the performance of fusion process are often real scene image or the equivalence, which hardly exists in practice. To avoid using the nonexistent real scene image, an alternative reference is created through extracting and merging edge features from each input image. Secondly, the performance measurement needs to be involved in optimization process. Our method adopts a simulated annealing procedure to optimize the Wavelet coefficients combination. As the objective function, the cumulative edge distance is minimized by adjusting the magnitude of the wavelet coefficients according to the error matrix, so as to approaching the optimal coefficient combination.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.