Abstract
In this paper, a new method for the problem of shape representation and classification is proposed. In this method, we define a radius function on the contour of the shape which captures for each point of the boundary, attributes of its related internal part of the shape. We call these attributes as “depth” of the point. Depths of boundary points generate a descriptor sequence which represents the shape. Matching of sequences is performed using dynamic programming method and a distance measure is acquired. At last, different classes of shapes are classified using a hierarchical clustering method and the distance measure.
The proposed method can analyze features of each part of the shape locally which this leads to the ability of part analysis and insensitivity to local deformations such as articulation, occlusion and missing parts. We show high efficiency of the proposed method by evaluating it for shape matching and classification of standard shape datasets.
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References
Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Regocnition 37, 1–19 (2004)
Blum, H.: A Transformation for extracting new descriptors of Shape. In: Whaten-Dunn, W. (ed.) Models for the perception of Speetch and Visual Forms, pp. 362–380. MIT Press, Cambridge (1967)
Siddiqi, K., Shokoufandehs, A., Dickinsons, S.J., Zucker, S.W.: Shock Graphs and Shape Matching. International Journal of Computer Vision 35(1), 13–32 (1999)
Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of Shapes by Editing Shock Graphs. In: ICCV 2001, pp. 755–762 (2001)
Bernier, T., Landry, J.-A.: A New Method for Representing and Matching Shapes of Natural Objects. Pattern Recognition 36, 1711–1723 (2003)
Kang, S.K., Ahmad, M.B., Chun, J.H., Kim, P.K., Park, J.A.: Modified Radius-Vector Function for Shape Contour Description. In: Laganà, A., Gavrilova, M., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3046, pp. 940–947. Springer, Heidelberg (2004)
Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition using Shape Contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(24), 509–522 (2002)
Thayananthan, A., Stenger, B., Torr, P.H.S., Cipolla, R.: Shape Context and Chamfer Matching in Cluttered Scenes. IEEE CVPR 1, 127–133 (2003)
Gorelick, L., Galun, M., Sharon, E., Basri, R., Brandt, A.: Shape Representation and Classification Using the Poisson Equation. IEEE Transaction on Pattern Recognition and Machine Intelligence 28(12), 1991–2005 (2005)
Navarro, G.: A guided tour to approximate string matching. ACM Computing Surveys (CSUR) 33(1), 31–88 (2000)
Kimia Image Database (May 2007), Available at http://www.lems.brown.edu/~dmc/main.html
MPEG7 CE Shape Database (May 2007), Available at http://www.imageprocessingplace.com/DIP/dip_image_dabases/image_databases.htm
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Zaboli, H., Rahmati, M. (2007). Shape Representation and Classification Using Boundary Radius Function. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_22
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DOI: https://doi.org/10.1007/978-3-540-76390-1_22
Publisher Name: Springer, Berlin, Heidelberg
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