Abstract
This paper presents the fusion of monogenic signal processing and differential geometry to enable monogenic analyzing of local intrinsic 2D features of low level image data. New rotational invariant features such as structure and geometry (angle of intersection) of two superimposed intrinsic 1D signals will be extracted without the need of any steerable filters. These features are important for all kinds of low level image matching tasks in robot vision because they are invariant against local and global illumination changes and result from one unique framework within the monogenic scale-space.
We acknowledge funding by the German Research Foundation (DFG) under the projects SO 320/4-2 and We 2602/5-1.
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Wietzke, L., Sommer, G., Schmaltz, C., Weickert, J. (2008). Differential Geometry of Monogenic Signal Representations. In: Sommer, G., Klette, R. (eds) Robot Vision. RobVis 2008. Lecture Notes in Computer Science, vol 4931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78157-8_35
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DOI: https://doi.org/10.1007/978-3-540-78157-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78156-1
Online ISBN: 978-3-540-78157-8
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