Abstract
In a previous paper we have presented the idea of representing the shape of a 2D object by scanning it following a Hilbert curve then performing wavelet smoothing and sampling. We also introduced the idea of using only a subset of the resulting signature for comparison purposes. We called that set the Key Feature Points (KFPs). In this paper we introduce the idea of taking the KFPs over a number of views of the original shape. The proposed improvement results in a significant increase in recognition rates when applied to the MPEG-7 and ETH-80 data sets when the Hilbert scan is used. Similar improvement is achieved when the raster scan is used.
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Ebrahim, Y., Ahmed, M., Chau, SC., Abdelsalam, W. (2010). Significantly Improving Scan-Based Shape Representations Using Rotational Key Feature Points. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2010. Lecture Notes in Computer Science, vol 6111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13772-3_29
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DOI: https://doi.org/10.1007/978-3-642-13772-3_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13771-6
Online ISBN: 978-3-642-13772-3
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