Concurrent Computation of Attribute Filters on Shared Memory Parallel Machines

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...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 30; no. 10; pp. 1800 - 1813
Main Authors: Wilkinson, M.H.F., Hui Gao, Hesselink, W.H., Jonker, J.-E., Meijster, A.
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.10.2008
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Cluster Analysis
Computation
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Concurrent computing
connected filters
Enhancement Parallel algorithms
Exact sciences and technology
Filtering
Filters
Fourier transforms
Gain
Image Enhancement - instrumentation
Image Enhancement - methods
Image Interpretation, Computer-Assisted - instrumentation
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - instrumentation
Imaging, Three-Dimensional - methods
Information Storage and Retrieval - methods
Intelligence
mathematical morphology
Microprocessors
Morphology
Parallel algorithms
Parallel machines
Parallel processing
Pattern analysis
Pattern Recognition, Automated - methods
Senior members
Shape
Software
Thinning
Tree graphs
connected filters
mathematical morphology
Enhancement Parallel algorithms
Filtering
algorithms
Parallel algorithm
Cache memory
Parallel machines
Distributed computing
Mathematical morphology
Morphological filter
Distributed memory
Concurrency
Morphological analysis
Minimax method
Parallelism
Tridimensional image
Attribute filters
Pattern analysis
Artificial intelligence
Shared memory
parallel computing
Single machine
Thickening
ISSN:0162-8828, 1939-3539
Online Access:Get full text
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Summary: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.
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ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2007.70836