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| 2D Shape Reconstruction Based on Combined Skeleton-Boundary Features
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Source |
International Journal of Image Processing (IJIP) |
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Table of Contents |
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Volume: 4 Issue: 4 |
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Pages: 287-455 |
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Publication
Date: October |
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ISSN
(Online): 1985-2304 |
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Pages |
293 - 306 |
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Author(s) |
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Published
Date |
30-10-2010 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
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| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: : Protrusion,, Symmetry-curvature duality, Bamboo boundary, merging point |
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| Reconstructing a shape into meaningful representation plays a strong role in shape-related applications. It is motivated by recent studies in visual human perception discussing the importance of certain shape boundary features as well as features of the shape area; it utilizes certain properties of the shape skeleton based on symmetry axes combined with boundary features based on curvature to determine protrusion strength. The main contribution of this paper is the combination of skeleton and boundary information by deploying the symmetry –curvature duality method to simulate human perception based on results of research in visual perception. The experiments directly compare our algorithm with experiments on human subjects. They show that the proposed approach meets the human perceptual intuition. In comparison to existing methods, our method gives a perceptually more reasonable and stable result. Furthermore, the noisy shape reconstruction demonstrates the robustness of our method, experiments of different data sets prove the invariant representation of the combined skeleton-boundary approach. |
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| 1 |
H. Blum. “A transformation for extracting new descriptors of shape, in Models for the Perception of Speech and Visual Form (W.Wathen-Dunn, ed.)”, Cambridge MA: MIT Press, pp. 362-380, (1967) |
|
|
| 2 |
J. K. Lakshmi and M. P. Valli. “A Survey on skeletons in digital image processing” . International conference proceedings of IEEE computer Society,2009 |
|
|
| 3 |
J. K. Lakshmi and M. P. Valli. “A Survey on skeletonization in digital image processing”. International conference proceedings of Managing Next Generations Software Applications08,Sponsored by CSIR, New Delhi, 2008 |
|
|
| 4 |
Cohen, E.H., Singh, M. “ Geometric determinants of shape segmentation: Tests using segment identification”. Vision Research 47: 2825–2840, 2007 |
|
|
| 5 |
Siddiqi, K., Kimia, B.B. ”Parts of visual form: Computational aspects”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17: 239–251, 1995 |
|
|
| 6 |
Bai, X., Latecki, L.J., Liu, W. “Skeleton pruning by contour partitioning with discrete curve evolution”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29: 449– 462, 2007 |
|
|
| 7 |
Bai, X., Latecki, L.J.. “Discrete skeleton evolution”. 6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition,2007 |
|
|
| 8 |
J. Komalalakshmi1, Dr. M. Punithavalli. “Impact of Boundary Points in Skeleton Based Images”. International journal of advanced engineering and applications,(IJAEA)Italy, 2010 |
|
|
| 9 |
J. Zeng, R. Lakaemper, X. Yang, Xin Li, Temple University, Philadelphia, PA. 2D “Shape Decomposition Based on Combined Skeleton-Boundary Features”, Lecture Notes in Computer Science. |
|
|
| 10 |
Aslan, C.; Erdem, A.; Erdem, E.; Tari, S.; Microsoft Corp., Redmond, WA “Disconnected Skeleton: Shape at Its Absolute Scale,pattern analysis and machine intelligence”. Ieee transaction on, IEEE computer society, 30(12): 2008 |
|
|
| 11 |
H. Chui, A. Rangarajan. “A new point matching algorithm for non-rigid registration”. CVIU, 89(2-3):114–141, 2003. |
|
|
| 12 |
W. Mio, A. Srivastava, and S. Joshi. “On shape of plane elastic curves”. Submitted to IJCV. |
|
|
| 13 |
A. Peter and A. Rangarajan. “Shape matching using the Fisher-Rao Riemannian metric: Unifying shape representation and deformation”. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 1164–1167, 2006 |
|
|
| 14 |
T. B. Sebastian, P. N. Klein, B. B. Kimia. “Recognition of shapes by editing their shock graphs”. IEEE Trans. Pattern Anal. Mach. Intell., 26(5):550–571, 2004 |
|
|
| 15 |
J. Shah. “An H2 type Riemannian metric on the space of planar curves”. Workshop on the Mathematical Foundations of Computational Anatomy (in conjunction with MICCAI’06), 2006 |
|
|
| 16 |
K. Leonard. “Classification of 2D shapes via efficiency measures”. Unpublished work, 2006. |
|
|
| 17 |
H. Blum. “Biological shape and visual science”. Journal of Theoretical Biology, 38:205–287, 1973 |
|
|
| 18 |
F. L. Bookstein. “Morphometric Tools for Landmark Data–Geometry and Biology”. Cambridge Univ. Press, 1991 |
|
|
| 19 |
V. Camion and L. Younes. “Geodesic interpolating splines”. In EMMCVPR, pp. 513–527, 2001 |
|
|
| 20 |
S. Belongie, J. Malik, J. Puzicha. “Shape matching and object recognition using shape contexts”. IEEE Trans. PatternAnal. Mach. Intell., 24(4):509–522, 2002 |
|
|
| 21 |
M. C. Burl and P. Perona. “Recognition of planar object classes”. In CVPR, pp. 223–230, 1996 |
|
|
| 22 |
H. Chui, A. Rangarajan. “A new point matching algorithm for non-rigid registration”. CVIU, 89(2-3):114–141, 2003 |
|
|
| 23 |
D. Geiger, T. L. Liu, R. V. Kohn. “Representation and self-similarity of shapes”. IEEE Trans. Pattern Anal. Mach.Intell.,25(1):86–99, 2003 |
|
|
| 24 |
K. Siddiqi, A. Shokoufandeh, S. J. Dickinson, S. W. Zucker. “Shock graphs and shape matching”. Int. J. Comput.Vision, 35(1):13–32, 1999 |
|
|
| 25 |
J. Glaunes, A. Trouve, and L. Younes. “Diffeomorphic matching of distributions: A new approach for unlabelled point-sets and sub-manifolds matching”. In CVPR, pp. 712–718, 2004 |
|
|
| 26 |
J. komalalakshmi, Dr .M. Punithavalli. “Discrete Skelton Reconstruction using bamboo skeleton communicated to the IEEE Transaction on Image processing”. |
|
|
| 27 |
M. Brady, H. Asada. “Smoothed local symmetries and their implementation”. The International Journal of Robotics Research, 3(3):36–61, 1984 |
|
|
| 28 |
J.W. Bruce, P.J. Giblin, C.G. Gibson. “Symmetry sets”. Proc. R. Soc. Edinb. Sect. A-Math, 101:163–186, 1985 |
|
|
| 29 |
D. Geiger, T. L. Liu, R. V. Kohn. “Representation and self-similarity of shapes”. IEEE Trans. Pattern Anal. Mach.Intell., 25(1):86–99, 2003 |
|
|
| 30 |
Bai, X., Latecki, L. “Path similarity skeleton graph matching”. IEEE Transactions on Pattern Analysis and Machine Intelligence 30 (2008) |
|
|
| 31 |
M. Leyton. “A process-grammar for shape”. Artificial Intelligence, 34(2):213–247, 1988 |
|
|
| 32 |
M. Brady, H. Asada. “Smoothed local symmetries and their implementation”. The International Journal of Robotics Research, 3(3):36–61, 1984 |
|
|
| 33 |
Michael Leyton. “Symmetry-Curvature Duality, computer vision ,graphics and image processing”. 38:327-341, 1987. |
|
|
| 34 |
J.k. lakshmi, Dr .M.Punithavalli. “Computation of merging points in skeleton based digital images”.GJCST, global journal of computer science and technology, 9(5):164-171, 2009. |
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| j.komala lakshmi : Colleagues
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| M. Punithavalli : Colleagues
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