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Enhancement of Shape Description and Representation by Slope

Authors: Ali Salem Bin Samma, Rosalina Abdul Salam


Representation and description of object shapes by the slopes of their contours or borders are proposed. The idea is to capture the essence of the features that make it easier for a shape to be stored, transmitted, compared and recognized. These features must be independent of translation, rotation and scaling of the shape. A approach is proposed to obtain high performance, efficiency and to merge the boundaries into sequence of straight line segments with the fewest possible segments. Evaluation on the performance of the proposed method is based on its comparison with established method of object shape description.

Keywords: Shape description, Shape representation and Slope.

Digital Object Identifier (DOI):

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[1] Zhang, D. and Lu, G. 2004. Review of shape representation and description techniques. Pattern Recognition 37: 1-19
[2] Jagadish, H.V., 1991. A retrieval technique for similar shapes. In: ACM SIGMOD Conf.: Management of Data, pp. 208-217.
[3] Sclaroff, S. and Pentland, A., 1995. modal matching for correspondence and recognition. IEEE Trans. Pattern Anal. Machine Intell. 17 (6), 545- 561.
[4] Kupeev, K.Y. and Wolfson, H.J., 1994. On shape similarity. In: Conf. on Pattern Recognition. Computer Society Press, pp. 227-231.
[5] Zhu, Y., De Silva, L.C. and Ko, C.C., 2002. Using moment invariants and HMM in facial expression recognition. Pattern Recognition Lett. 23, 83-91.
[6] Niblack, W. et al, 1993. QBIC project: Querying images by content using color, texture and shape. In: Conf. of Storage and Retrieval for Image and Video Databases, San Jose, California, February.
[7] Mohamad, D., Sulong, G. and Ipson, S.S., 1995. Trademark matching using invariant moments. In: Li, S. et al. (Eds.), Second Asian Conf. on Computer Vision. Springer, Singapore, pp. I-439-I-444.
[8] Lu, G. and Sajjanhar, A., 1999. Region-based shape representation and similarity measure suitable for content-based image retrieval. Multimedia Systems 7 (2), 165-174.
[9] El-Rube, I., Kamel, M. and Ahmed, M., 2004. Wavelet-based affine invariant shape matching and classification. In: ICIP Internat. Conf. on Image Processing, Singapore.
[10] Cortelazzo, G. et al., 1994. Trademark shapes description by string matching techniques. Pattern Recognition 27 (8), 1005-1018.
[11] Basri, R., Costa, L., Geiger, D. and Jacobs, D., 1998. Determining the similarity of deformable shapes. Vision Res. (38), 2365-2385.
[12] Sebastian, T.B. and Kimia, B.B., 2001. Curves vs. skeletons in object recognition. In: Internat. Conf. on Image Processing, pp. 22-25.
[13] Sebastian, T.B., Klein, P.N. and Kimia, B.B., 2003. A survey of shape analysis techniques. IEEE Trans. Pattern Anal. Machine Intell. 25 (1), 116-125.
[14] Binsamma A. S. and AbdulSalam R. Adaptation of K Means Algorithm for Image Segmentation. International Journal of Signal Processing 5:4 2009.
[15] Gopal, T. and Prasad, V. A Novel Approach to Shape Based Image Retrieval Integrating Adapted Fourier Descriptors and Freeman Code. IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.6, June 2008.
[16] Stahl, J. S. and Wang, S. 2008. Open Boundary Capable Edge Grouping with Feature Maps. IEEE Transactions on Image Processing, 978 1 4244 2340.