Abstract
We present a new framework for hierarchical segmentation of multidimensional multivariate functions into homogeneous regions. Homogeneity is defined as constancy of n-th order derivatives (called features) of the function. The degree of similarity (measure of homogeneity) is used as a scale parameter to obtain a stack of segmentations. Hierarchical segmentation is represented as a tree which contains the geometric and topological information about the detected regions. Detected regions preserving their information in the tree over large range of scales are selected into a pyramid representation. Results showing noise robustness and computational efficiency of the proposed method are presented. Experiments to compare the method with three other segmentation techniques and applications to two- and three-dimensional images having one-, three- and six-variate data are described for the zeroth and first order region features.
This research was supported in part by Advanced Research Projects Agency under grant N00014-93-1-1167 and National Science Foundation under grant IRI 93-19038.
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© 1997 Springer-Verlag Berlin Heidelberg
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Bajcsy, P., Ahuja, N. (1997). A new framework for hierarchical segmentation using similarity analysis. In: ter Haar Romeny, B., Florack, L., Koenderink, J., Viergever, M. (eds) Scale-Space Theory in Computer Vision. Scale-Space 1997. Lecture Notes in Computer Science, vol 1252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63167-4_62
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DOI: https://doi.org/10.1007/3-540-63167-4_62
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