\begin{frame}
	\myheading{Module 12.1 : Introduction to object detection}
\end{frame}

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\begin{frame}
	\begin{itemize}
		\justifying
		\item So far we have looked at Image Classification
		\item We will now move on to another Image Processing Task - \textit{Object Detection}
	\end{itemize}
\end{frame}

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\begin{frame}
	\begin{overlayarea}{\textwidth}{\textheight}
		
		\vspace{1cm}
		\input{modules/Module1/tikz_images/car_bounding_box.tex}
		
		% \begin{tabular}{p{1.6cm} p{3.5cm} p{3.5cm} p{3.5cm} }
		% \onslide<2->{ Task} & \onslide<2->{Image classification} & \onslide<4->{Object Detection} & \onslide<6->{Image Segmentation}\\ 
		%  \onslide<3->{Output} & \onslide<3->{Car} & \onslide<5->{Car, exact bounding box containg} & \onslide<7->{Roughly at object boundaries} \\  
		%  \onslide<8->{Annotations} & \onslide<9->{\hspace{+0.1cm}Relatively easier} & \onslide<10->{Relatively easy} & \onslide<11->{Difficult} \\
		% \end{tabular}
		
		\begin{tabular}{p{1.6cm} p{6cm} p{3cm} }
			\onslide<2->{ \textbf{Task}}  & \onslide<2->{Image classification} & \onslide<4->{Object Detection}                       \\ 
			\onslide<3->\textbf{{Output}} & \onslide<3->{Car}                  & \onslide<5->{Car, exact bounding box containing car} \\  
		\end{tabular}
		
	\end{overlayarea}
\end{frame}

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\begin{frame}
	\begin{overlayarea}{\textwidth}{\textheight}
		
		\input{modules/Module1/tikz_images/region_proposals_1.tex}
		\begin{itemize}
			\justifying
			\only<1-2> {\item<1-> Let us see a typical pipeline for \it{object detection}
				\item<2-> It starts with a region proposal stage where we identify potential regions which may contain objects
			}
			\only<3-4>{
				\item<3-> We could think of these regions as mini-images
				\item<4-> We extract features(SIFT, HOG, CNNs) from these mini-images
			}
			\only<5->{
				\item<5-> Pass these through a standard image classifer to determine the class
			}
		\end{itemize}
	\end{overlayarea}     
	
\end{frame}

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\begin{frame}
	\begin{overlayarea}{\textwidth}{\textheight}
		\input{modules/Module1/tikz_images/region_proposals_regression.tex}
		
		\begin{itemize}
			\justifying
			\item<1-> In addition we would also like to correct the proposed bounding boxes
			\item<2-> This is posed as a regression problem (for example, we would like to predict $w^*$, $h^*$ from the proposed $w$ and $h$)
		\end{itemize}
		
	\end{overlayarea}     
\end{frame}

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\begin{frame}
	\begin{columns}
		\column{0.5\textwidth}
		\begin{overlayarea}{\textwidth}{\textheight}
			
			\input{modules/Module1/tikz_images/faster_rcnn.tex}
		\end{overlayarea}
		\column{0.5\textwidth}
		\begin{overlayarea}{\textwidth}{\textheight}
			\begin{itemize}
				\justifying
				\item<1-> Let us see how these three components have evolved over time
				\item<2-> Propose all possible regions in the image of varying sizes (almost brute force)
				\item<3-> Use handcrafted features (SIFT, HOG)
				\item<4-> Train a linear classifier using these features
				\item<5-> We will now see three algorithms that progressively improve these components 
			\end{itemize}
		\end{overlayarea}
	\end{columns}
\end{frame}

