Howtosignalprocessing
- Why the second derivative is useful for edge detection?
- What is the difference between first-order and second order derivative in edge detection?
- What is first-order edge detection?
- What is the disadvantage of using a second order derivative filters for edge detection?
Why the second derivative is useful for edge detection?
The 2nd derivative of an image where the image highlights regions of rapid intensity change and is therefore often used for edge detection zero crossing edge detectors.
What is the difference between first-order and second order derivative in edge detection?
The first-order derivatives are good to select the stongest edges by (hysteresis-)thresholding the gradient magnitude. The zero-crossings of the second-order derivatives are good for localization of the edge.
What is first-order edge detection?
Edge detection is the technique used to identify the regions in the image where the brightness of the image changes sharply. This sharp change in the intensity value is observed at the local minima or local maxima in the image histogram, using the first-order derivative.
What is the disadvantage of using a second order derivative filters for edge detection?
However there are disadvantages to the use of second order derivatives. (We should note that first derivative operators exaggerate the effects of noise.) Second derivatives will exaggerated noise twice as much. No directional information about the edge is given.
