Image enhancement and thresholding by optimization of fuzzy compactness
Pattern Recognition Letters
Finding local maxima in a pseudo-Euclidean distance transform
Computer Vision, Graphics, and Image Processing
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVGIP: Graphical Models and Image Processing
On digital distance transforms in three dimensions
Computer Vision and Image Understanding
Fuzzy distance transform: theory, algorithms, and applications
Computer Vision and Image Understanding
Synthesising Objects and Scenes Using the Reverse Distance Transformation in 2D and 3D
ICIAP '95 Proceedings of the 8th International Conference on Image Analysis and Processing
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Fuzzy distance based hierarchical clustering calculated using the a∗ algorithm
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
Morphological grayscale reconstruction in image analysis: applications and efficient algorithms
IEEE Transactions on Image Processing
Aspects on the reverse fuzzy distance transform
Pattern Recognition Letters
Image processing system for localising macromolecules in cryo-electron tomography
Machine Graphics & Vision International Journal
Hi-index | 0.10 |
A decomposition scheme for 3D fuzzy objects is presented. The decomposition is guided by a fuzzy distance transform (FDT) of the fuzzy object and aims to decompose the fuzzy object into simpler parts. Relevant voxels, corresponding to the ''centres'' of the parts, are detected on the FDT and suitably grouped, using a hierarchical clustering technique, into significant seeds for the decomposition. A region growing process is then applied to the seeds. The region growing process makes use of the reverse fuzzy distance transform, which is introduced in this manuscript. The decomposition scheme is illustrated using real data from different applications of which one, namely the identification of the three parts of the Immunoglobulin G antibody imaged using cryo electron tomography, is described more in detail.