我有一个列表，我想在每个列表项后显示一个数字。我正确地得到了前两个数字，但最后两个数字不正确。这是我的代码：
\begin{enumerate}[leftmargin=0cm,itemindent=.5cm,labelwidth=\itemindent,labelsep=0cm,align=left]
\item
\textbf {Chi-squared distance :} The chi squared distance measures the distance between two histograms having identical bins (16 bins along each dimension). Moreover, both histograms are normalized, i.e. their entries sum up to one. The distance measure $\bf d$ is usually defined (although alternative definitions exist) as \[ d(H_1,H_2) = \Sigma \frac{ (H_1(I)-H_2(I))^{2} }{ (H_1(I)+H_2(I)) }\]The name of the distance is derived from Pearson's chi squared test statistic $ X^{2} (x,y) = \Sigma( (x_i-y_i)^{2} / x_i)$ for comparing discrete probability distributions (i.e histograms). However, unlike the test statistic, $d(H_1,H_2)$ is symmetric wrt. $H_1$ and $H_2$. The histograms obtained for superpixels are very sparse and only bins with non-zero entries in atleast one of the two histograms are considered. Thus, the bins with zero entries in both $H_1$ and $H_2$ histograms are discarded. An example of obtained chi-squared distance values is as follows:
\begin{figure}[!h]
\centering
\includegraphics [width=90mm]{chi_sqr.png}
\caption{Variation of chi squared distance for a superpixel over consecutive image frames}
\end{figure}
\item
\textbf{Correlation :} Here, the correlation or similarity between two histograms having identical bins is calculated. A higher value of correlation indicates more similarity and thus similar labels. If the correlation value for a superpixel drops below a certain threshold, a new label should be assigned to that superpixel. Correlation is calculated using \[ d(H_1,H_2) = \frac{ \Sigma_I (H_1(I)-\overline H_1)(H_2(I)-\overline H_2) }{\sqrt{ \Sigma_I (H_1(I)-\overline H_1)^{2}\Sigma_I (H_2(I)-\overline H_2)^{2}} }\] where \[ \overline H_k = \frac{1}{N}\Sigma_j H_k(j)\]
\begin{figure}[!h]
\centering
\includegraphics [width=90mm]{correl.png}
\caption{Correlation values for a superpixel over consecutive image frames}
\end{figure}
\item
\textbf{Mean squared distance :} MS distance is the sum of the squared differences of the histogram bins. It is computed as follows: \[ d(H_1, H_2) = \Sigma_I (H_1(I) - H_2(I))^2\]
\begin{figure}[!h]
\centering
\includegraphics [width=90mm]{mse.png}
\caption{Variation of mean squared distance for a superpixel over consecutive image frames}
\end{figure}
\item
\textbf{Average Color :} Here, the average color of the region enclosed by each superpixel is computed. For LAB input image, avergae color is caluclated for each channel (L, A, B) separately. The color difference between superpixels in different frames, $d(F_1, F_2)$ is obtained using \[d(F_1, F_2) = (L_{F_1} - L_{F_2})^2 + (A_{F_1} - A_{F_2})^2 + (B_{F_1} - B_{F_2})^2\]
\begin{figure}[!h]
\centering
\includegraphics [width=90mm]{avg_color.png}
\caption{Variation of average color difference for a superpixel over consecutive image frames}
\end{figure}
\end{enumerate}
请告诉我如何在代码中指定的位置获取我的图形。
答案1
以下是我展示两个图的方式，但您可以在图中展示任意多个图。这是我正在输入的一些笔记的摘录，但您可以将其用作示例。
\begin{figure}[H]
\centering
\subfloat[\(e = 0.65\)]{\includegraphics[width = 3.25in]
{lagrangeeccen65n3n10.eps}}
\subfloat[\(e = 0.90\)]{\includegraphics[width = 3.25in]
{lagrangeeccen90n3n10.eps}}
\caption{The Lagrange series for eccentricities below and above the Laplace
limit.}
\label{fig:fig26}
\end{figure}
您可以随时插入\begin{tabular}{cccc}\end{tabular}并在一列中格式化 4 张图片，然后根据需要拥有任意数量的行。