Abstract
Multiscale feature extraction in image data has been investigated for many years. More recently the problem of processing images containing irregularly distribution data has became prominent. We present a multiscale Laplacian approach that can be applied directly to irregularly distributed data and in particular we focus on irregularly distributed 3D range data. Our results illustrate that the approach works well over a range of irregular distributed and that the use of Laplacian operators on range data is much less susceptive to noise than the equivalent operators used on intensity data.
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Suganthan, S., Coleman, S., Scotney, B. (2008). Multiscale Laplacian Operators for Feature Extraction on Irregularly Distributed 3-D Range Data. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds) Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, vol 5008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79547-6_39
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DOI: https://doi.org/10.1007/978-3-540-79547-6_39
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