Parallel Algorithm for Concurrent Computation of Connected Component Tree
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
A New Fuzzy Connectivity Measure for Fuzzy Sets
Journal of Mathematical Imaging and Vision
Fast fuzzy connected filter implementation using max-tree updates
Fuzzy Sets and Systems
Parallel image thinning through topological operators on shared memory parallel machines
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Interactive segmentation based on component-trees
Pattern Recognition
Towards a parallel topological watershed: first results
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
Concurrent computation of differential morphological profiles on giga-pixel images
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
Component-Trees and Multivalued Images: Structural Properties
Journal of Mathematical Imaging and Vision
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Morphological attribute filters have not previously been parallelized, mainly because they are both global and non-separable. We propose a parallel algorithm which achieves efficient parallelism for a large class of attribute filters, including attribute openings, closings, thinnings and thickenings, based on Salembier's Max-Trees and Min-trees. The image or volume is first partitioned in multiple slices. We then compute the Max-trees of each slice using any sequential Max-Tree algorithm. Subsequently, the Max-trees of the slices can be merged to obtain the Max-tree of the image. A C-implementation yielded good speed-ups on both a 16-processor MIPS 14000 parallel machine, and a dual-core Opteron-based machine. It is shown that the speed-up of the parallel algorithm is a direct measure of the gain with respect to the sequential algorithm used. Furthermore, the concurrent algorithm shows a speed gain of up to 72% on a single-core processor, due to reduced cache thrashing.