JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Feasibility Study of a Distributed and Parallel Environment for Implementing the Standard Version of AAM Model
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Feasibility Study of a Distributed and Parallel Environment for Implementing the Standard Version of AAM Model
Naoui, Moulkheir; Mahmoudi, Said; Belalem, Ghalem;
  PDF(new window)
 Abstract
The Active Appearance Model (AAM) is a class of deformable models, which, in the segmentation process, integrates the priori knowledge on the shape and the texture and deformation of the structures studied. This model in its sequential form is computationally intensive and operates on large data sets. This paper presents another framework to implement the standard version of the AAM model. We suggest a distributed and parallel approach justified by the characteristics of the model and their potentialities. We introduce a schema for the representation of the overall model and we study of operations that can be parallelized. This approach is intended to exploit the benefits build in the area of advanced image processing.
 Keywords
Active Appearance Model;Data Parallelism;Deformable Model;Distributed Image Processing;Parallel Image Processing;Segmentation;
 Language
English
 Cited by
 References
1.
T. F. Cootes, C. J. Taylor, H. Cooper, and J. Graham, "Active shape models-their training and application," Computer Vision and Image Understanding, vol. 61, no. 1, pp. 38-59, 1995. crossref(new window)

2.
M. G. Roberts, T. F. Cootes, and J. E. Adams, "Linking sequences of active appearance sub-models via constraints: an application in automated vertebral morphometry," in Proceeding of the British Machine Vision Conference (BMVC2003), Norwich, UK, 2003 pp. 349-358.

3.
A. L. Scheinine, M. Donizelli, and M. Pescosolido, "An object oriented client server system for interactive segmentation of medical images using the method of active contours," in Bildverarbeitung für die Medizin 1998. Heidelberg: Springer, 1998, pp. 308-312.

4.
M. B. Stegmann, "Generative interpretation of medical images," Ph.D. dissertation, Technical University of Denmark, 2004.

5.
A. Taguemount, L. Djema, and F. O. Boumghar, "Traitement parallèle sous MPI-2 pour l'accélération de l'algorithme d'extraction de contours d'images," in Proceedings of the 3rd International Conference: Sciences of Electronic, Technologies of Information and Telecommunications (SETIT 2005), Tunisia, 2005.

6.
S. Yan, X. Hou, S. Z. Li, H. Zhang, and Q. Cheng, "Face alignment using view-based direct appearance models," International Journal Imaging Systems and Technology, vol. 13, no. 1, pp. 106-112, 2003. crossref(new window)

7.
S. Yan, C. Liu, S. Z. Li, H. Zhang, H. Y. Shum, and Q. Cheng, "Face alignment using texture-constrained active shape models," Image Vision Computing, vol. 21, no. 1, pp. 69-75, 2003. crossref(new window)

8.
R. Oji, "An automatic algorithm for object recognition and detection based on ASIFT keypoints," Journal of Signal & Image Processing, vol. 3, no. 5, pp. 29- 39, 2012.

9.
I. M. Scott, T. F. Cootes, and C. J. Taylor, "Improving appearance model matching using local image structure," in Information Processing in Medical Imaging. Heidelberg: Springe, 2003, pp.258-269.

10.
T. F. Cootes and C. J. Taylor, "Statistical models of appearance for computer vision," Imaging Science and Biomedical Engineering, University of Manchester, UK, Technical Report, 2004. http://www.face-rec.org/algorithms/AAM/app_models.pdf.

11.
T. F. Cootes and C. J. Taylor, "On representing edge structure for model matching," in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, 2001, pp. 1114- 1119.

12.
T. F. Cootes, G. J. Edwards, and C. J. Taylor, "Active appearance models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 681-685, 2001. crossref(new window)

13.
J. Sung, T. Kanade, and D. Kim, "A unified gradient-based approach for combining ASM into AAM," International Journal of Computer Vision, vol. 75, no. 2, pp. 297-309, 2007. crossref(new window)

14.
M. B. Stegmann, B. K. Ersboll, and R. Larsen, "FAME: a flexible appearance modeling environment," IEEE Transactions on Medical Imaging, vol. 22, no. 10, pp. 1319-1331, 2003. crossref(new window)

15.
T. F. Cootes, G. J. Edwards, and C. J. Taylor, "Active appearance models," in Computer Vision - ECCV'98. Heidelberg: Germany, 1998, pp. 484-498.

16.
O. E. K. Naoui, G. Belalem, and S. Mahmoudi, "A reflexion on implementation version for active appearance model," International Journal of Computer Vision and Image Processing, vol. 3, no. 3, pp. 16-30, 2013.

17.
A. L. Jackson, "A parallel algorithm for fast edge detection on the graphics processing unit," Ph.D. dissertation, Department of Computer Science, Washington and Lee university, VA, 2009.

18.
S. Mahmoudi, P. Manneback, C. Augonnet, and S. Thibault, "Detection optimale des coins et contours dans des bases d'images volumineuses sur architectures multicoeurs heterogenes," in Rencontres francophones du parallelisme (RenPar'20), Sint-Malo, France, 2011.

19.
A. Andrecut, "Parallel GPU implementation of iterative PCA algorithms," Journal of Computational Biology, vol. 16, no. 11, pp. 1593-1599, 2009. crossref(new window)

20.
M. W. Berry, D. Mezher, B. Philippe, and A. Sameh, "Parallel algorithms for the singular value decomposition," in Statistics Textbooks and Monographs. Boca Raton, FL: CRC Press, 2006, pp. 117-164.

21.
P. N. Happ, R. Q. Feitosa, C. Bentes, and R. Farias, "A parallel image segmentation algorithm on GPUs," in Proceedings of the 4th Conference on Geographic Object-Based Image Analysis (GEOBIA), Rio de Janeiro, Brazil, 2012, pp. 580-589.

22.
J. M. Morel and G. Yu, "ASIFT: a new framework for fully affine invariant image comparison," SIAM Journal on Imaging Sciences, vol. 2, no. 2, pp. 438-469, 2009. crossref(new window)

23.
T. Saidani, "Optimisation multi-niveau d'une application de traitement d'images sur machines paralleles," Ph.D. dissertation, Universite Paris Sud-Paris XI, France, 2012.

24.
O. E. K. Naoui, S. Mahmoudi, and G. Belalem, "Towards a distributed and parallel schema for active appearance model implementation," International Journal of Computational Vision and Robotics, vol. 6, no. 1-2, pp. 19-40, 2016. crossref(new window)

25.
C. Goodall, "Procrustes methods in the statistical analysis of shape," Journal of the Royal Statistical Society Series B, vol. 53, no. 2, pp. 285-339, 1991.

26.
R. Larsen, "L1 generalized Procrustes 2D shape alignment," Journal of Mathematical Imaging and Vision, vol. 31, no. 2-3, pp. 189-194, 2008. crossref(new window)