Abstract
A robust method is presented for retrieval of model shapes that have parts similar to the query shape presented to the image database. Structural feature indexing is a potential approach to efficient shape retrieval from large databases, but it is sensitive to noise, scales of observation, and local shape deformations. To improve the robustness, shape feature generation techniques are incorporated into structural indexing based on quasi-invariant shape signatures. The feature transformation rules obtained by an analysis of some particular types of shape deformations are exploited to generate features that can be extracted from deformed patterns. Effectiveness is confirmed through experimental trials with databases of boundary contours, and is validated by systematically designed experiments with a large number of synthetic data.
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© 2001 Springer-Verlag Berlin Heidelberg
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Nishida, H. (2001). Robust Structural Indexing through Quasi-Invariant Shape Signatures and Feature Generation. In: Arcelli, C., Cordella, L.P., di Baja, G.S. (eds) Visual Form 2001. IWVF 2001. Lecture Notes in Computer Science, vol 2059. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45129-3_64
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DOI: https://doi.org/10.1007/3-540-45129-3_64
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