Advertisement

Range Image Segmentation on a Cluster

  • Mary Ellen Bock
  • Concettina Guerra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2552)

Abstract

We report on the implementation of a range image segmentation approach on a cluster using Message Passing Interface (MPI). The approach combines and integrates different strategies to find the best fitting planes for a set of three dimensional points. There are basically three distint modules for plane recovery; each module has a distinct method for generating a candidate plane and a distinct objective function for evaluating the candidate and selecting the ”best” plane among the candidates. Parallelism can be exploited in two different ways. First, all three modules can be executed concurrently and asynchronously by distinct processes. The scheduling of the modules in the parallel implementation differs significantly from that of the sequential implementation. Thus, different output images can be obtained for the two implementations. However, the experiments conducted on severalra nge images show that on average the quality of results is similar in comparison with ground truth images. Second, the computation within each module can be performed in parallel. A module chooses the best plane among a large set of randomly selected candidate planes; this is a highly parallel task that can be efficiently partitioned among a group of processors. The approach proposed in this paper for a multiprocessor environment has been implemented on a cluster of workstations using MPI. Preliminary results are presented.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    A. Apostolico, V. Breton, E. Cornillot, S. Du, L. Duret, C. Gautier, C. Guerra, N. Jacq, R. Medina, C. Michau, J. Montagnat, A. Robinson, M. Senger. DataGrid. requirements for grid-aware biology applications. Tech. report.Google Scholar
  2. [2]
    M.E. Bock, C. Guerra. “A geometric approach to the segmentation of range images”, Proceedings of the Second International Conference on 3D-Digital Imaging and Modeling, Ottawa, Canada, pp. 261–269, 1999.Google Scholar
  3. [3]
    M.E. Bock, C. Guerra. “Segmentation of range images through the integration of different strategies”, 6th Int.Work.Vision, Modeling, and Visualization, Stuttgart, Germany, 2001.Google Scholar
  4. [4]
    J. Bruck, D. Dolev, C.T. Ho, M. Rosu, R. Strong, “Efficient message passing interface (MPI) for parallel computing on clusters of workstations”, Journal of Parallel and Distributed Computing, v. 40, pp. 19–34, 1997.CrossRefGoogle Scholar
  5. [5]
    M.A. Fischler and R.C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM 24, pp. 381–395, June 1981.Google Scholar
  6. [6]
    A. Hoover, J. B. Gillina, X. Jiang, P. Flynn, H. Bunke, D. Goldgolf, K. Bowyer, D. Eggert, A. Fitzgibbon, and R. Fischer, An experimentalcom parison of range image segmentation algorithms,” IEEE Trans. on Pattern Analysis and Machine Intelligence 18(7), pp. 673–689, 1996.CrossRefGoogle Scholar
  7. [7]
    X. Jiang, K. Bowyer, Y. Morioka, S. Hiura, K. Sato, S. Inokuchi, M. Bock, C. Guerra, R.E. Loke, J.M.H. du Buf. “Some Further Results of Experimental Comparison of Range Image Segmentation Algorithms”, 15th Int. Conference on Pattern Recognition, Spain, 2000.Google Scholar
  8. [9]
    P. J. Morrow and D. Crookes and J. Brown and G. McAleese and D. Roantree and I. Spence, “Efficient implementation of a portable parallel programming model for image processing”, Concurrency-Practice and Experience, 11, 11, pp. 671–685, 1999.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Mary Ellen Bock
    • 1
  • Concettina Guerra
    • 2
  1. 1.Dept of StatisticsPurdue UniversityWest-Lafayette
  2. 2.Dip. Ingegneria dell’InformazioneUniversità di PadovaPadovaItaly

Personalised recommendations