Howtosignalprocessing
- What is first-order derivative in edge detection?
- How first-order and second order derivative helps in edge detection?
- What is the disadvantage of using a second order derivative filters for edge detection?
- Why are second order edge detectors better at finding edges than first-order detectors?
What is first-order derivative in edge detection?
In this method we take the 1st derivative of the intensity value across the image and find points where the derivative is maximum then the edge could be located. The gradient is a vector, whose components measure how rapid pixel value are changing with distance in the x and y direction.
How first-order and second order derivative helps 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 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.
Why are second order edge detectors better at finding edges than first-order detectors?
Generally 2nd derivative is more sensitive to noise than 1st derivative. The 2nd derivative is usually accompanied by zero crossing detection, so it works better when grey level transitions are smooth.
