AI Article Synopsis
- Morphological attribute filters, typically global and non-separable, have not been parallelized, but the proposed algorithm allows for efficient parallel processing of these filters using Salembier's Max-Trees and Min-trees.
- The algorithm involves partitioning the image or volume into slices, computing the Max-trees for each slice sequentially, and then merging them to form the complete Max-tree for the image.
- A C-implementation demonstrated significant speed-ups on both a 16-processor machine and a dual-core machine, with speed gains of up to 72 percent on single-core processors attributed to reduced cache thrashing.
Article Abstract
Morphological attribute filters have not previously been parallelized, mainly because they are both global and non-separable. We propose a parallel algorithm that 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 percent on a single-core processor, due to reduced cache thrashing.
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| http://dx.doi.org/10.1109/TPAMI.2007.70836 | DOI Listing |
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