
Estoy usando látex para colocar la primera figura en la parte superior central de la página que abarca las dos columnas, justo después de esta figura quiero agregar otras 2 figuras con algunos párrafos y ecuaciones a la misma página en el entorno de dos columnas:
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure*}
\centering
\includegraphics[width=3.0in]{Image1}
\caption{\scriptsize{Fig. 1. (a) Intensity profile extracted from a real image. (b) The estimated 1st derivative information. (c) The estimated contrast information with$ \sigma = 2:0.$}}
\label{Fig1}
\end{figure*}
%
\begin{figure}
\begin{centering}
\includegraphics[width=0.75\columnwidth]{Image3}
\caption{\scriptsize{Fig. 2. Definition of contrast.}}
\label{Fig2}
\end{centering}
\end{figure}
By convoluting a profile $ I(x) $ with this operator, we have
\begin{equation}
\label{eq:3}
\varphi (x)=I(x)\otimes B(x)= I(x) - I(x)\otimes N(x;0,\sigma).
\end{equation}
Basically,$\varphi (x)$ can be imagined as the 2nd derivative of the profile I(x). Moreover, the local extremes of $ \varphi (x)$ correspond to the high-curvature points of $I(x)$. Fig. 1(c) shows the contrast information extracted from Fig. 1(a) using (3) with$\sigma = 2.0$ , which is determined empirically. It is obvious that Fig. 1(c) offers much more reliable information than Fig. 1(b).\\
Since the 2nd derivative is orientation-dependent, the contrast information at an image pixel has to be measured along various orientations. In the proposed algorithm, we detect boundaries by checking the relations between each pixel and its eight neighbors. Hence, four directional operators are used at each pixel to measure the curvature information at that pixel. These four directions are 0 , 45 , 90 and 135 , respectively. All these four directional contrast data are then grouped together in subsequent processes.
\section{\small{COLOR CONTRAST IN THE CIE LAB COLOR SPACE}}
In color image segmentation, a proper choice of color space is also a crucial issue. In the selection of color space, we choose the CIE $ L^*a^*b^* $ color space to work on due to its three major properties: $1)$ separation of achromatic information from chromatic information, $ 2)$ uniform color space, and $ 3) $ similar to human visual perception $[12]$. Here,$ L^*$ represents the luminance component, while $ a^* $ and $ b^* $ represent color components. The formulae for converting an RGB image into the coordinates can be found in many color-related articles, like $[12]$ and $[13]$.\\
\begin{figure}
\centering
\includegraphics[width=2.0in]{Image2}
\caption {\scriptsize{Fig. 3. Example of the test pattern in the subjective experiment.}}
\label{Fig3}
\end{figure}
In the CIE color space, the Euclidean distance between and , defined as $(4)$ is approximately equivalent to the perceptual difference between these two colors $[4]$, $[12]$. By incorporating this color difference formula into our contrast definition, we define the color contrast across an edge as$(5)$ To further explore the correlation between color contrast and the luminance level or color level, we made a subjective experiment. In our experiment, $10$ observers are involved and the patterns are displayed over a calibrated ViewSonic PT775 monitor for comparisons. Here, the values of luminance/color contrast are coarsely quantized into eleven steps, 0, 5, 10, 15, …, 50. In
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\end{document}
El resultado deseado se parece a esta imagen:
Se agradecerá cualquier ayuda o sugerencia para lograr este trabajo. Pasé mucho tiempo haciendo eso sin ningún éxito. Soy novato en el látex. Atentamente.
Respuesta1
Si agrego \lipsum[1-10]
( \usepackage{lipsum}
justo antes del código que proporcionaste y corrijo el código de la figura, obtengo exactamente lo que pareces querer.
Solo me cambié times
( mathptmx
el primero está obsoleto).
\documentclass[10pt,a4paper,twoside,twocolumn]{article}
\usepackage[utf8]{inputenc}
\usepackage[left=0.53in,right=0.83in,top=0.3in,bottom=1.7in]{geometry}
%\usepackage[hmarginratio=1:1]{geometry}
\usepackage{fancyhdr}
%\usepackage{multicol}
\usepackage{mathptmx}
\usepackage{lettrine}
\usepackage{graphicx}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{amsthm}
\usepackage{etoolbox}% http://ctan.org/pkg/etoolbox
\usepackage{sectsty}
\usepackage{lipsum}
%\usepackage{float}
%\usepackage[hang]{footmisc}
%
\pagestyle{fancy}
\renewcommand\thesection{\Roman{section}.}
%\renewcommand\thesubsection{\thesection.\arabic{subsection}}
\setlength{\columnsep}{0.43cm}
\setlength{\parindent}{0.16in}
%\setlength\footnotemargin{10pt} %
%\footnotesep is the space between footnotes:
%\setlength{\footnotesep}{0.5cm}
%\footins is the space between the text body and the footnotes:
\setlength{\skip\footins}{0.70cm}
\renewcommand*\footnoterule{}
%\pagestyle{myheadings}
%\pdfpagewidth 8.5in
%\pdfpageheight 11in
\headheight 55pt
%\footerheight 55pt
%\rhead{\scriptsize{\thepage}}
%\chead{Middle top}
%\lhead{\scriptsize{IEEE SIGNAL PROCESSING LETTERS, VOL. 11, NO. 7, JULY 2004}}
\fancyhead[LE,RO]{\scriptsize{\thepage}}
\fancyhead[LO,RE]{\scriptsize{IEEE SIGNAL PROCESSING LETTERS, VOL. 11, NO. 7, JULY 2004}}
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%\lfoot{Left bottom}
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% \noindent\makebox[0.1em][r]{\@makefnmark}#1}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Alter some LaTeX defaults for better treatment of figures:
% See p.105 of "TeX Unbound" for suggested values.
% See pp. 199-200 of Lamport's "LaTeX" book for details.
% General parameters, for ALL pages:
\renewcommand{\topfraction}{0.9} % max fraction of floats at top
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% Parameters for TEXT pages (not float pages):
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\setcounter{totalnumber}{4} % 2 may work better
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{document}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\lipsum[1-10]
%%%%%%%% ORIGINAL CODE (fixed)
\begin{figure*}
\centering
\includegraphics[width=3.0in]{Image1}
\caption{(a) Intensity profile extracted from a real image. (b) The estimated 1st
derivative information. (c) The estimated contrast information with $\sigma = 2:0.$}
\label{Fig1}
\end{figure*}
\begin{figure}[t]
\centering
\includegraphics[width=0.75\columnwidth]{Image3}
\caption{Definition of contrast.}
\label{Fig2}
\end{figure}
By convoluting a profile $ I(x) $ with this operator, we have
\begin{equation}
\label{eq:3}
\varphi (x)=I(x)\otimes B(x)= I(x) - I(x)\otimes N(x;0,\sigma).
\end{equation}
Basically, $\varphi (x)$ can be imagined as the 2nd derivative of the profile I(x).
Moreover, the local extremes of $ \varphi (x)$ correspond to the high-curvature points of
$I(x)$. Fig. 1(c) shows the contrast information extracted from Fig. 1(a) using (3)
with$\sigma = 2.0$ , which is determined empirically. It is obvious that Fig. 1(c) offers
much more reliable information than Fig. 1(b).\\ Since the 2nd derivative is
orientation-dependent, the contrast information at an image pixel has to be measured along
various orientations. In the proposed algorithm, we detect boundaries by checking the
relations between each pixel and its eight neighbors. Hence, four directional operators are
used at each pixel to measure the curvature information at that pixel. These four
directions are 0 , 45 , 90 and 135 , respectively. All these four directional contrast data
are then grouped together in subsequent processes. \section{\small{COLOR CONTRAST IN THE
CIE LAB COLOR SPACE}} In color image segmentation, a proper choice of color space is also a
crucial issue. In the selection of color space, we choose the CIE $ L^*a^*b^* $ color space
to work on due to its three major properties: $1)$ separation of achromatic information
from chromatic information, $ 2)$ uniform color space, and $ 3) $ similar to human visual
perception $[12]$. Here,$ L^*$ represents the luminance component, while $ a^* $ and $ b^*
$ represent color components. The formulae for converting an RGB image into the coordinates
can be found in many color-related articles, like $[12]$ and $[13]$.\\
\begin{figure}
\centering
\includegraphics[width=2.0in]{Image2}
\caption{Example of the test pattern in the subjective experiment.}
\label{Fig3}
\end{figure}
In the CIE color space, the Euclidean distance between and , defined as $(4)$ is
approximately equivalent to the perceptual difference between these two colors $[4]$,
$[12]$. By incorporating this color difference formula into our contrast definition, we
define the color contrast across an edge as$(5)$ To further explore the correlation between
color contrast and the luminance level or color level, we made a subjective experiment. In
our experiment, $10$ observers are involved and the patterns are displayed over a
calibrated ViewSonic PT775 monitor for comparisons. Here, the values of luminance/color
contrast are coarsely quantized into eleven steps, 0, 5, 10, 15, …, 50. In
\end{document}
Tenga en cuenta que tenía figure
en lugar de figure*
; Es irrelevante qué opciones especifiques, porque figure*
solo irá en la parte superior.
Dejé una línea en blanco antes y después de cada uno de los entornos de las figuras. Lo que debes hacer, cuando el documento esté en su forma final en lo que al texto se refiere, es
coloque el código en
figure*
algún lugar entre dos párrafos en la página que precede a donde lo deseaMueva hacia arriba o hacia abajo en el documento los dos
figure
entornos hasta que estén donde desee.
El segundo punto no debería requerir demasiado trabajo.