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Is it an open door to
common parallelization strategy
for topological operators on SMP machines ?
R. MAHMOUDI – A3SI Lab.
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Summary
PhD Objectives
Scientific and technical context
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Scientific and technical context (1)
Image processing operators
Dynamic redistribution
Thresholding
Point-to-Point operators
Associated class
Linear filters Opening Thinning
Crest restoring
Smoothing
Watershed
Closing
Local operators
Morphological operators
Topological operators
Globaloperators
FourierTransformation
Euclidean DistanceTransformation
Not-linear filters Attributed
Filter
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Point-to-Point operators
(Associated class) Vs (Parallelization strategies)
Local operators
Morphological operators
Topological operators
Globaloperators
Sienstra [1](2002)
Wilkinson [2](2007)
[1] F. J. Seinstra, D. Koelma, and J. M. Geusebroek, “A software architecture for user transparent parallel image processing”.[2] M.H.F. Wilkinson, H. Gao, W.H. Hesselink, “Concurrent Computation of Attribute Filters on Shared Memory Parallel Machines”.[3] A. Meijster, J. B. T. M. Roerdink, and W. H. Hesselink, “A general algorithm for computing distance transforms in linear time” .
Meijster [3]
Scientific and technical context (2)
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To
po
log
ica
l op
era
tors
Thinning operator [1]
Crest restoring [1]
2D and 3D smoothing [2]
Watershed based on w-thinning [3]
Watershed based on graph [4]
Homotopic kernel transformation [5]
Leveling kernel transformation [5]
[1] M. Couprie, F. N. Bezerra, and G. Bertrand, “Topological operators for grayscale image processing”,
[2] M. Couprie, and G. Bertrand, “Topology preserving alternating sequential filter for smoothing 2D and 3D objects”.
[3] G. Bertrand, “On Topological Watersheds”.
[4] J. Cousty, M. Couprie, L. Najman and G. Betrand “Weighted fusion graphs: Merging properties and watersheds”.
[5] G. Bertrand, J. C. Everat, and M. Couprie, "Image segmentation through operators based on topology“
commonparal le l i zat ion
strategy
PhD Objectives (1)
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PhD Objectives (2)
Shared Memory Machine
CPU1
CPU2
CPU3
CPUn
Random Access Memory
MIMD Machine :
(Execute several instruction streams in parallel on different data)
Main Architectural Classes
SISD
machines
SIMD
machines
MISD
machines
Distributed
Memory
System
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C o m m o n p a r a l l e l i z a t i o n s t r a t e g y o f t o p o l o g i c a l o p e r a t o r s o n m u l t i - c o r e
m u l t i t h r e a d a r c h i t e c t u r e ( M I M D M a c h i n e s – S h a r e d M e m o r y S y s t e m ) ?
1. Unifying parallelization method of topological operators class (Algorithmic level)
2. Implementation Methodology and optimization techniques on multi-core multithread
architecture (Architecture level).
Needs
Main Objectives
PhD Objectives (3)
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www.mramzi.net
More details
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