나는 라텍스를 사용하여 두 열에 걸쳐 있는 페이지의 상단 중앙에 첫 번째 그림을 배치하고 있습니다. 이 그림 바로 뒤에 몇 개의 단락과 방정식이 포함된 다른 그림 2개를 두 열 환경의 동일한 페이지에 추가하고 싶습니다.
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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}
원하는 출력은 다음 이미지와 같습니다.
이 작업을 수행하는 데 도움이나 힌트를 주시면 감사하겠습니다. 나는 그것을 하기 위해 많은 시간을 허비했지만 성공하지 못했습니다. 저는 라텍스 초보자입니다. 친애하는.
답변1
\lipsum[1-10]제공한 코드 바로 앞에 추가 \usepackage{lipsum}하고 그림 코드를 수정하면 원하는 것을 정확하게 얻을 수 있습니다.
나는 단지 (전자는 더 이상 사용되지 않음) times로 변경되었습니다.mathptmx
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\usepackage{mathptmx}
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\usepackage{lipsum}
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\renewcommand\thesection{\Roman{section}.}
%\renewcommand\thesubsection{\thesection.\arabic{subsection}}
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%\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
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\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}
대신 figure에 figure*; figure*맨 위에만 표시되므로 어떤 옵션을 지정하는지는 중요하지 않습니다 .
각 그림 환경 앞뒤에 빈 줄을 남겼습니다. 텍스트에 관한 한 문서가 최종 형식에 있을 때 수행해야 할 작업은 다음과 같습니다.
figure*원하는 위치 앞의 페이지에서 두 단락 사이 어딘가에 코드를 배치하십시오.figure원하는 위치에 도달할 때까지 두 환경을 문서에서 위 또는 아래로 이동합니다.
두 번째 요점은 너무 많은 노력이 필요하지 않습니다.
