\documentclass[a4paper, onecolumn, 11pt]{article}

\usepackage{graphicx}
\usepackage[caption=false,font=footnotesize]{subfig}
\usepackage{amsmath}
\usepackage{algorithm}
\usepackage{algorithmic}
\usepackage{xcolor, colortbl}
\usepackage{soul}
\usepackage{enumerate}
\usepackage[top=3cm,right=2.5cm,bottom=2.5cm,left=3cm]{geometry} 
\usepackage{setspace}

\rmfamily
\doublespacing


\begin{document}


 Fig. \ref{fig:seenArea} illustrates this fact and shows how training half-ellipsoids in $\theta_i=0^\circ,+30^\circ,+60^\circ,+90^\circ$ contribute in generating $\theta_{new}=+40^\circ$ pose. It clearly shows that the smaller the $|\theta_{new} - \theta_i|$, the larger part of $HE_{\theta_i}$ can be seen in the camera. As the figure implies, $HE_{30}$ plays the main role in generating $\theta_{new}=+40^\circ$ pose while parts of $HE_0$ and $HE_{90}$ are not visible in pose $\theta_{new}=+40^\circ$.
\begin{figure}
    \centering
    \includegraphics[width=0.45\textwidth]{./4.eps}
    \caption{Part of half ellipsoids in $\theta_i=0^\circ,+30^\circ,+60^\circ,+90^\circ$ degrees that can be
    seen from camera in $\theta_{new}=+40^\circ$.}
    \label{fig:seenArea}
\end{figure}
Thus, construction of every new pose is mainly done by high contribution of its near poses. When a point can be seen from several training $HE_{\theta_i}$s, the weighted sum of its corresponding points on these $HE_{\theta_i}$s determines its gray level. The weight for contribution of each $HE_{\theta_i}$ is proportional to $\frac{1}{|\theta_{new}-\theta_i|}$ and the weight function can be chosen from the family of reciprocal functions.


\end{document}