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Concurrent computation of attribute filters on shared memory parallel machines.

Michael H F Wilkinson¹, Hui Gao, Wim H Hesselink

¹Institute for Mathematics and Computing Science, University of Groningen, AK Groningen, The Netherlands. m.h.f.wilkinson@rug.nl

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 16, 2008

Summary

This summary is machine-generated.

This study introduces a novel parallel algorithm for morphological attribute filters, overcoming previous limitations. The new method efficiently processes image and volume data, achieving significant speed-ups on multi-processor systems.

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Area of Science:

Image Processing
Computer Vision
Parallel Computing

Background:

Morphological attribute filters are essential image processing tools.
Previous attribute filters were difficult to parallelize due to their global and non-separable nature.

Purpose of the Study:

To develop an efficient parallel algorithm for a wide range of morphological attribute filters.
To enable faster processing of image and volume data using parallel computing.

Main Methods:

The proposed algorithm partitions images/volumes into slices.
Max-Trees and Min-Trees, based on Salembier's work, are computed for each slice.
Slice Max-Trees are merged to form the complete image Max-Tree.

Main Results:

A C-implementation demonstrated significant speed-ups on parallel machines (16-processor MIPS, dual-core Opteron).
The parallel algorithm achieved up to 72% speed gain on a single-core processor due to reduced cache thrashing.
Speed-up directly correlates with the gain over sequential algorithms.

Conclusions:

The developed parallel algorithm effectively addresses the parallelization challenges of morphological attribute filters.
This approach offers substantial performance improvements for image and volume processing tasks.
The method is applicable to attribute openings, closings, thinnings, and thickenings.