ÄS mW: S
64X64 128X128 256X256 512X512 1024X1024
This segmentation algorithm has the obvious parallel functional
characteristics.
Implementation of the algorithm in four PC nodes which consist
of parallel programming environment is achieved, and the
different nodes of computing time and computational speedup
are analysis. There are specific terms of time and speed up as
shown in fig 1.
Fig 1 the implementation time of different size of image
applying growth algorithm
The algorithm is to use a series of seed through "data
parallelism" to get the first division of the image. Then based on
these parameters such as the divided region area, the average
gray-scale by using "functional parallel", the image
segmentation is completed. By analyzing figure 1, it is
indicated that the greater image data, the greater the time
speedup. Practice shows that the algorithm has good parallelism.
5. CONCLUTION AND DISCUSSIONS
Faced with the remote sensing data growing geometrically, it is
an important topic to research efficient and fast image
segmentation by regional growth. This paper summarizes the
traditional regional growth segmentation algorithm. And the
algorithm which suit for "data parallelism" and was raised
based on grid environment. The practical result proves that the
algorithm is suitable. The algorithm both in theory and in
practice provides useful guidance and reference for image
segmentation.
Although growth image segmentation by regional has been a
great deal of research results, but there are still some problems
of parallel algorithm to be solved.
(1) Seed selection and the effective growth criteria is still a hot
and focus research spot.
(2) Due to the application of this algorithm is still confined in
small and single-band images, it is very necessary to study
feasible and efficient parallel segmentation algorithm for the
massive or multi-band remote sensing images.
(3) Using "data parallelism" which Fully consider all the data-
band and "functional parallel" which analysis the correlation
among bands, the accuracy of the segmentation is expected to
improve.
REFERENCE
[1] Zhang Y.J., 2001. Image Segmentation. Science Press,
Beijing, pp67-70.
[2] Yu Z.H., Chen Y., Liu P., 2003. Grid Computing. QingHua
Press, Beijing, pp6-12.
[3] Michael J. Quinn, 2004. Parallel Programming in C with
MPI and OpenMP. QingHua Press, Beijing, pp 102-145.
[4] Smith, J., 1987a. Close range photogrammetry for analyzing
distressed trees. Photogrammetria, 42(1), pp. 47-56.
[5] Nico, Giovanni; Fusco, Luigi; Linford, Julian, 2002. Grid
technology for the storage and processing of remote sensing
data: Description of an application. Proceedings of SPIE - The
International Society for Optical Engineering, v 4881, pp. 677-
685.
[6] JaeHo Jeon, Hyung-Sun Kim, GeonYoung Choi and
HyunWook Park, 2000. KAIST image computing system
(KICS): A parallel architecture for real-time multimedia data
processing. Journal of Systems Architecture, 46(15), pp. 1403-
1418.
[7] S. Boussakta, 1999. A novel method for parallel image
processing applications. Journal of Systems Architecture,
45(10), pp. 825-839.
[8] Zhou H.F., Jiang Y.H., Yang X.J., 2002. Research on Serial
and Parallel Strategies of Watershed Transform. JOURNAL OF
NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY,
24(6), pp. 71-76.
[9] Zhou H.F., Liu G.M., Zheng M.L., 2004. A Research on
Serial and Parallel Strategies of the Automatic Image
Registration for Remote Sensing. JOURNAL OF NATIONAL
UNIVERSITY OF DEFENSE TECHNOLOGY, 26(2), pp. 57-
61.
[10] A. Bevilacqua and E. Loli Piccolomini. Parallel image
restoration on parallel and distributed computers. Parallel
Computing, Volume 26, Issue 4, March 2000, Pages 495-506.
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