1. A Parallel Matching Algorithm Based on Image Gray Scale Liang Zong, Yanhui Wu cso, vol. 1, pp.109-111, 2009 International Joint Conference on Computational Sciences and Optimization, 2009 邱惠琪 (Huei Chi Chiu) 2009-12-17

2. OUTLINE I.Introduction II.Gray scale correlation matching III.Parallel model and implementation IV.Experiment results V.Conclusions

3. I. Introduction It widely used in a variety of areas such as the computer, the medical images and the aircraft guidance. Fingerprint enrollment Strange image Minutia matching Results

4. I. Introduction  Why do we need parallel processing ? It's real-time processing system. It requires substantial computation. can shorten the overhead of gray scale matching significantly high speedup and efficiency can be acquired.  Under the parallel environment we must consider the parallel feasibility of the problem.

5. II. Gray scale correlation matching The know image is the template, given by. The strange image given by. Expansion as follows:

6. II. Gray scale correlation matching Takes the maximum the D(i 0, j 0 ) will takes the minimal, therefore the most accurate location is (i, j)

7. MPI – Can be transferred form the traditional supercomputer to the cluster system. 3. Parallel model and implementation

8. III. Parallel model and implementation  Analyze the model of serial processing, find the largest part of calculation and analyze whether it can be parallel processing. The image pixel is a two-dimensional array and the matching deals with the pixels point by point, it could be considered as parallel processing.

9. III. Parallel model and implementation We can divide the strange image into p data blocks, each block has a continuous r row vectors, r =[M/p]. Step1 :

10. The master node sends P data blocks with “MPI_Send ( )” to the p slave nodes that marked the 0, 1, …(p-1). Step2 : Master node : sending p data blocks, accepting the results of the slave nodes calculate and calculating the results of the first block. Slave node : accept ( r + N-1) row vectors. III. Parallel model and implementation

11. The slave nodes marked 1, 2, 3... (p-1) accept data blocks which the master node sends with “MPI_Recv ( )”. The nodes marked 1, 2, 3... (p-2) accept ( r + N-1) row vectors and the final node (p-1) accepts [M-(p-1) * r ] row vectors. Each node calculates the R(i, j) and sends the results to the master node with “MPI_Send ( )”. Step3 :

12. IV. Experiment results The cluster is composed by 4 computers. The configurations as follows:  The master node CPU: Celeron 2.00GHz, Memory: 384M.  The slave nodes CPU: P4 1.5GHz, Memory: 256M.

13. IV. Experiment results Strange image Template 32x32Template 64x64

14. node Image size1234 256x2561.530.830.590.42 512x5126.814.712.531.91 1024x102432.5118.0312.019.34 Overhead of template 32x32 (S) IV. Experiment results

15. node Image size1234 256x2564.312.741.621.23 512x51223.8613.428.647.18 1024x1024116.2972.5948.6334.73 Overhead of template 64x64 (S) IV. Experiment results

16. Speedup of template 32x32 IV. Experiment results

17. Parallel efficiency of template 64x64 IV. Experiment results

18. V. Conclusions In this paper, we propose an improved parallel algorithm for image gray scale matching. The algorithm is suitable for calculation intensive problems that usually spend much time on computation. Experiment results show that image gray scale matching is accurate. The algorithm can be used as a reference to image parallel processing. Our further works will focus on improving and optimizing the algorithm for better performance.

19. Q & A ?

20. THANKS !!