1971
DOI: 10.1109/proc.1971.8136
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Techniques for edge detection

Abstract: I I v -I L ----_ -_ _ _ 2 2 I D E A L VOLTAOE AW.FlER Fig. 1. RC active filter with ideal voltage amplifier.The choice of D(s) may be so made that the pole sensitivity due to change in the gain of the voltage amplifier is minimum. Considering a secondorder transfer function, letHswhere the poles s1 and s: are at -r k j x . The H may be assumed equal to unity without loss of generality so thatFor this second-order transfer function, let D(s) be of first order having a real negative root. Therefore,Then.In terms… Show more

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Cited by 68 publications
(35 citation statements)
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“…After obtaining the optimal threshold levels for the grayscale image, multiple binary images are computed for each threshold level, using equation (1).…”
Section: Optimal Threshold Level Selectionmentioning
confidence: 99%
See 1 more Smart Citation
“…After obtaining the optimal threshold levels for the grayscale image, multiple binary images are computed for each threshold level, using equation (1).…”
Section: Optimal Threshold Level Selectionmentioning
confidence: 99%
“…This results in less precise localisation of the identified edges. However, all the edges do not contain a step variation in intensity, because the properties such as refraction or poor focus can result in objects with borders defined by a gradual variation of intensity (Argyle & Rosenfeld, 1971). Many algorithms have been suggested for analysing image intensity variation, including statistical methods (Nahi & Assefi, 1972;Huang & Tseng, 1988;Stern & Kurz, 1988), difference methods (Prewitt, 1970;Marr & Hildreth, 1984) and curve fitting methods (Haralick, 1984;Nalwa & Binford, 1986).…”
Section: Introductionmentioning
confidence: 99%
“…A few techniques are accessible for their programmed calculation [9], yet by and large their qualities must be altered by the client. A noteworthy issue of LoG is that the limitation of edges with a topsy-turvy profile by zero-intersection focuses presents an inclination which increments with the smoothing impact of sifting [10].An fascinating answer for this issue was proposed by Canny [11], which says in an ideal operator for step edge location incorporates three criteria: great recognition, great confinement, and stand out reaction to a solitary edge.…”
Section: Figurementioning
confidence: 99%
“…Edge detection is one of the very basic concepts used for image processing to identify the target elements. The edge detection techniques are basically of two types; gradientbased and Laplacian-based techniques (Argyle, 1971;Grimson and Hildreth, 1985;Torre and Poggio, 1986;Canny, 1986). While the former detects the edges from the gradient (first derivative) of pixel intensities, the latter detects edges from zero crossings in the second-order derivative of pixel intensities.…”
Section: Image Processing and Target Cloud Identificationmentioning
confidence: 99%