-edges in images.
-The most common ones are filter
-edge detection methods such as Sobel or Canny filters, low order methods such as
-first order and second order ones. These methods are based on gradient or
-Laplace operators and fuzzy edge methods, which are based on fuzzy logic to
-highlight edges.
-
-Of course, all the algorithms have advantages and drawbacks that depend on the
-motivations behind that edges detection. Unfortunately unless testing most of the
-algorithms, which would require many times, it is quite difficult to have an
-accurate idea on what would produce such algorithm compared to another. That is
-why we have chosen Canny algorithm, which is well known, fast, and implementable
+edges in images (whose noise has been initially reduced).
+They can be separated in two categories: first and second order detection
+methods on the one hand, and fuzzy detectors in the second hand~\cite{KF11}.
+In first order methods like Sobel,
+a first-order derivative (gradient magnitude, etc.) is computed
+to search for local maxima, whereas in second order ones, zero crossings in a second-order derivative, like the Laplacian computed from the image,
+are searched in order to find edges.
+For fuzzy edge methods, they are obviously based on fuzzy logic to highlight
+edges.
+Canny filters, on their parts, are an old family of algorithms still remaining a state-of-the-art edge detector. They can be well approximated by first-order derivatives of Gaussians.
+%%
+%
+%Of course, all the algorithms have advantages and drawbacks that depend on the
+%motivations behind that edges detection. Unfortunately unless testing most of the
+%algorithms, which would require many times, it is quite difficult to have an
+%accurate idea on what would produce such algorithm compared to another.
+%That is
+%why we have chosen
+As Canny algorithm is well known and studied, fast, and implementable
