Abstract
This paper investigates the scale selection problem for vector-valued nonlinear diffusion scale-spaces. We present a new approach for the localization scale selection, which aims at maximizing the image content’s presence by finding the scale having a maximum correlation with the noise-free image. For scale-space discretization, we propose to address an adaptation of the optimal diffusion stopping time criterion introduced by Mrázek and Navara [1], in such a way that it identifies multiple scales of importance.
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Mihai, C., Vanhamel, I., Sahli, H., Katartzis, A., Pratikakis, I. (2007). Scale Selection for Compact Scale-Space Representation of Vector-Valued Images. In: Sgallari, F., Murli, A., Paragios, N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2007. Lecture Notes in Computer Science, vol 4485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72823-8_4
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DOI: https://doi.org/10.1007/978-3-540-72823-8_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72822-1
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