Automatic gradient threshold determination for edge detection

We describe a method to automatically find gradient thresholds to separate edge from nonedge pixels. A statistical model that is the weighted sum of two gamma densities corresponding to edge and nonedge pixels is used to identify a threshold. Results closely match human perceptual thresholds even un...

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Veröffentlicht in:	IEEE transactions on image processing 1996-05, Vol.5 (5), p.784-787
Hauptverfasser:	Henstock, P.V., Chelberg, D.M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied sciences Detection algorithms Exact sciences and technology Histograms Humans Image edge detection Image processing Image segmentation Information, signal and communications theory Maximum likelihood estimation Parameter estimation Pixel Shape Signal processing Signal to noise ratio Telecommunications and information theory
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Beschreibung
Zusammenfassung:	We describe a method to automatically find gradient thresholds to separate edge from nonedge pixels. A statistical model that is the weighted sum of two gamma densities corresponding to edge and nonedge pixels is used to identify a threshold. Results closely match human perceptual thresholds even under low signal-to-noise ratio (SNR) levels.
ISSN:	1057-7149 1941-0042
DOI:	10.1109/83.499917