1. Is it an open door to common parallelization strategy for topological operators on SMP machines ? R. MAHMOUDI – A3SI Lab. 1

2. 2 Summary Scientific and technical context PhD Objectives

3. 3 Scientific and technical context (1) Image processingoperators Fourier Transformation Opening Thinning Dynamic redistribution Linear filters Closing Crest restoring Not-linear filters Euclidean Distance Transformation Thresholding Smoothing Attributed Filter Watershed Associated class Topological operators Morphological operators Local operators Point-to-Point operators Global operators R. MAHMOUDI – A3SI Laboratory– 2009 April

4. 4 Scientific and technical context (2) (Associated class) Vs (Parallelizationstrategies) Global operators Topological operators Morphological operators Local operators Point-to-Point operators Sienstra [1] (2002) Wilkinson [2] (2007) Meijster [3] [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” . R. MAHMOUDI – A3SI Laboratory– 2009 April

5. 5 PhD Objectives (1) Topological operators Thinning operator [1] common parallelization strategy 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“

6. 6 PhD Objectives (2) Main Architectural Classes SISD machines SIMD machines MISD machines MIMD Machine : (Execute several instruction streams in parallel on different data) Shared Memory Machine Distributed Memory System CPU1 CPU2 CPU3 CPUn Random Access Memory

7. 7 PhD Objectives (3) Needs Common parallelization strategy of topological operators on multi-core multithread architecture (MIMD Machines – Shared Memory System)? Main Objectives Unifyingparallelizationmethod of topologicaloperators class (Algorithmiclevel) Implementation Methodology and optimization techniques on multi-core multithread architecture (Architecture level).

8. More details www.mramzi.net 8

9. 9