Abstract
Active Shape Models often require a considerable number of training samples and landmark points on each sample, in order to be efficient in practice. We introduce the Fractal Active Shape Models, an extension of Active Shape Models using fractal interpolation, in order to surmount these limitations. They require a considerably smaller number of landmark points to be determined and a smaller number of variables for describing a shape, especially for irregular ones. Moreover, they are shown to be efficient when few training samples are available.
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Manousopoulos, P., Drakopoulos, V., Theoharis, T. (2007). Fractal Active Shape Models. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_80
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DOI: https://doi.org/10.1007/978-3-540-74272-2_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74271-5
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