Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Journal of Mathematical Imaging and Vision
Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
On Generalized Entropies and Scale-Space
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Entropy and Multiscale Analysis: A New Feature Extraction Algorithm for Aerial Images
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
The hierarchical structure of images
IEEE Transactions on Image Processing
Visual Analytics for model-based medical image segmentation: Opportunities and challenges
Expert Systems with Applications: An International Journal
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In image registration, mutual information is a well-performing measure based on principles of uncertainty. Similarly, in image analysis the Gaussian scale space, based on minimal assumptions of the image, is used to derive intrinsic properties of an image. This paper starts an investigation of a combination of both methods. This combination results in a double parameterized mutual information measure using local information of the image. For single modality matching best response is found for coinciding parameters. Then critical values are found for which the parameterized mutual information has extrema. First results on multi-modality matching show that different parameter values instead of coinciding values yield the best response for the parameterized mutual information.