Abstract
Techniques for an automatic detection of spiral grain in cross-section CT images of logs are proposed and evaluated on sets of natural and artificial cross-section images. Explicit analysis of global rotation, block matching, and optical flow techniques are compared. Experimental results seem to indicate that spiral grain in fact cannot be modeled by a circular motion of luminance values in gray scale images.
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Entacher, K., Lenz, C., Seidel, M., Uhl, A., Weiglmaier, R. (2007). Applicability of Motion Estimation Algorithms for an Automatic Detection of Spiral Grain in CT Cross-Section Images of Logs. 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_5
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DOI: https://doi.org/10.1007/978-3-540-74272-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74271-5
Online ISBN: 978-3-540-74272-2
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