X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/5e6f9e49a26cec4970434722a595a7b44197120f..e109e0d899bc233ae4407afe3af6ca03e1afe90a:/ourapproach.tex

diff --git a/ourapproach.tex b/ourapproach.tex
index 9626562..ccd297b 100644
--- a/ourapproach.tex
+++ b/ourapproach.tex
@@ -43,7 +43,21 @@ Image Quality: Edge Image Steganography
 \JFC{Raphael, les fuzzy edge detection sont souvent utilisÃ©s. 
   il faudrait comparer les approches en terme de nombre de bits retournÃ©s,
   en terme de complexitÃ©. } \cite{KF11}
-
+\RC{Ben, Ã  voir car on peut choisir le nombre de pixel avec canny. Supposons que les fuzzy edge soient retourne un peu plus de points, on sera probablement plus dÃ©tectable...  Finalement on devrait surement vendre notre truc en : on a choisi cet algo car il est performant en vitesse/qualitÃ©. Mais on peut aussi en utilisÃ© d'autres :-)}
+
+There are  many techniques to  detect edges in  images. Main methods  are filter
+edge detection methods such as Sobel  or Canny filter, low order methods such as
+first order  and second order  methods, 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 which depend on the
+motivation  to  highlight  edges.   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
+on many  kinds of architecture, such  as FPGA, smartphone,  desktop machines and
+GPU. And of course, we do not pretend that this is the best solution.
 
 Presentation des algos de detection de contour
 CaractÃ©ristiques
@@ -68,4 +82,4 @@ BBS-based cryptographic version of the message
 \input{stc}
 
 
-\subsection{Data Extraction}
\ No newline at end of file
+\subsection{Data Extraction}
