$O(n^2 +2.343^2 + 2\times 343^2 \times n^2 + 2.n^2 \ln(n))$, \textit{i.e}
$O(2.n^2(343^2 + \ln(n)))$.
-Our edge selection is based on a Canny Filter,
-whose complexity is in $O(2n^2.\ln(n))$ thanks to the convolution step
-which can be implemented with FFT.
+Our edge selection is based on a Canny Filter. When applied on a
+$n \times n$ square image the Noise reduction steps is in $O(5^3 n^2)n$.
+Next, let $T$ be the size of the canny mask.
+Computing gradients is in $O(4Tn)$ since derivatives of each direction (vertical or horizontal)
+are in $O(2Tn)$.
+Finally, thresholding with hysteresis is in $O(n^2)$.
+The overall complexity is thus in $O((5^3+4T+1)n^2)$.
To summarize, for the embedding map construction, the complexity of Hugo is
-at least $343^2/\ln{n}$ times higher than
-our scheme. For instance, for a squared image with 4M pixel per slide,
-this part of our algorithm is more than 14100 faster than Hugo.
+dramatically higher than our scheme.
We are then left to express the complexity of the STC algorithm.
According to~\cite{DBLP:journals/tifs/FillerJF11}, it is
-\documentclass{comjnl}
+\documentclass[twocolumn]{svjour3} % twocolumn
\usepackage{epsfig,psfrag}
\usepackage{graphicx}
\usepackage{color}
\author{Jean-Fran\c cois Couchot, Raphael Couturier, and Christophe Guyeux\thanks{Authors in alphabetic order}}
-\affiliation{ FEMTO-ST Institute, UMR 6174 CNRS\\
+\institute{ FEMTO-ST Institute, UMR 6174 CNRS\\
Computer Science Laboratory DISC,
University of Franche-Comt\'{e},
Besan\c con, France.}
\email{\{jean-francois.couchot, raphael.couturier, christophe.guyeux\}@univ-fcomte.fr}
-\shortauthors{J.-F. Couchot, R. Couturier, and C. Guyeux}
+
+\date{Received: date / Accepted: date}
+
-\received{...}
-\revised{...}
-
+\maketitle
\begin{abstract}
a scheme that can reasonably face up-to-date steganalysers.
\end{abstract}
-\maketitle
+
