On significant maxima detection: a fine-to-coarse algorithm
We suggest two efficient algorithms for the detection of 'perceptual significance' among the local maxima of a 1D signal. For an input function f(x) and an integer n, the first algorithm finds the n most significant maxima of f(x). The second algorithm finds all significant maxima of f(x),...
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Format: | Tagungsbericht |
Sprache: | eng |
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Zusammenfassung: | We suggest two efficient algorithms for the detection of 'perceptual significance' among the local maxima of a 1D signal. For an input function f(x) and an integer n, the first algorithm finds the n most significant maxima of f(x). The second algorithm finds all significant maxima of f(x), irrespective of their number. We represent an input signal f(x) as a G-graph G(f(x)), the vertices of which represent the 'lumps' of the graph of f(x). G(f(x)) is gradually reduced via a 'small leaf' trimming procedure, resulting in a sequence of graphs that give a hierarchy of representations of f(x) such that the leaves of 'deeper' graphs correspond to more significant maxima of f(x). Based on this hierarchy, we introduce a measure of 'perceptual significance' among the maxima of f(x) Experimental results are presented. |
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ISSN: | 1051-4651 2831-7475 |
DOI: | 10.1109/ICPR.1996.546831 |