Applications of spatial data structures: Computer graphics, image processing, and GIS
Applications of spatial data structures: Computer graphics, image processing, and GIS
Elements of information theory
Elements of information theory
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Multiresolution image compression with BSP trees and multilevel BTC
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Alignment by maximization of mutual information
Alignment by maximization of mutual information
Image segmentation using information bottleneck method
IEEE Transactions on Image Processing
Hierarchical Classifier Design Using Mutual Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
A generalized divergence measure for robust image registration
IEEE Transactions on Signal Processing
On the convexity of some divergence measures based on entropy functions
IEEE Transactions on Information Theory
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
Using Spanning Graphs for Efficient Image Registration
IEEE Transactions on Image Processing
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This paper presents a new approach for image registration based on the partitioning of the two source images in binary-space and quadtree structures, obtained with a maximum mutual information gain algorithm. Two different implementation approaches that differ in the level at which information is considered have been studied. The first works at pixel level using the simplified images directly, while the second works at node level dealing with the tree data structure. The obtained results show an outstanding accuracy and robustness of the proposed methods. In particular, the use of binary-space partitioned images drastically reduces the grid effects in comparison with regular downsampled images. An important advantage of our approach comes from the reduced size of the data structures corresponding to the simplified images, which makes this method appropriate to be applied in a multiresolution framework and telemedicine applications.