Title :
Automatic gradient threshold determination for edge detection
Author :
Henstock, Peter V. ; Chelberg, David M.
Author_Institution :
Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA
fDate :
5/1/1996 12:00:00 AM
Abstract :
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
Keywords :
edge detection; gamma distribution; statistical analysis; automatic gradient threshold determination; edge detection; gamma densities; human perceptual threshold; low signal-to-noise ratio levels; nonedge pixels; statistical model; Detection algorithms; Histograms; Humans; Image edge detection; Image segmentation; Maximum likelihood estimation; Parameter estimation; Pixel; Shape; Signal to noise ratio;
Journal_Title :
Image Processing, IEEE Transactions on
