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\title{\huge CS 223B Computer Vision\\Midterm Topics}
\date{}
\maketitle

\noindent Dear CS223b students,
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In order to help you study for the midterm exam, we have compiled below a list of topics that we expect you to have learned so far. Some topics are bolded, indicating a greater emphasis placed on them. The midterm exam questions will come from any of the following topics. But it is reasonable to assume that the majority of the questions will be selected from the bolded topics.
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We hope this helps!
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Fei-Fei

\section{Face Recognition}
\begin{itemize}
	\item \textbf{Principle Component Analysis (PCA), eigenfaces (set up the problem; know the 		practical algorithm; know why eigenvalue/vector is a solution)}
	\item Difference between Eigenfaces (PCA) vs. Fisherfaces (LDA) (theory and algorithm)
	\item \textbf{Boosting (algorithm level)}
	\item \textbf{Definitions of recognition problems: difference between classification and detection}
\end{itemize}

\section{Image Structure}
\begin{enumerate}
  \item Linear Filters:
  	\begin{itemize}
		\item Linear Shift Invariance
		\item Convolution, \textbf{Correlation}, Discrete Fourier Transform
		\item Sampling, \textbf{Aliasing}, Nyquist Sampling theorem
		\item Blurring, Smoothing, Low pass filtering
		\item \textbf{Pyramid Representation (algorithm for down-sampling an image)}
	\end{itemize}
  \item Finding lines:
	\begin{itemize}	
		\item \textbf{Canny Edge Dectection (algorithm level)}
		\item \textbf{Hough Transform, pros and cons}
		\item RANSAC, pros and cons 
	\end{itemize}
	
  \item Segmentation and Clustering:
	\begin{itemize}	
		\item Gestalt principles and cues
		\item \textbf{k-means clustering}
		\item \textbf{mean-shift segmentation}
		\item \textbf{Mixture of Guassians, Expectation Maximization (EM) algorithm }
		\begin{itemize}
			\item \textbf{Be able to compare and contrast the above methods (theory and understand when each should be used)}
		\end{itemize}
		\item Spectral clustering
		\item \textbf{Normalized cut (theory and practical approximation, how to set up the problem, and obtain eigensolutions)}	
	\end{itemize}	
\end{enumerate}


\section{Camera models}
\begin{itemize}

	\item Pinhole Camera Geometry
	\item \textbf{Homogeneous Coordinates}
	\item \textbf{Different camera models: weak, affine, projective} 
	\begin{itemize}
		\item How are parallel lines mapped?
	\end{itemize}
	\item Vanishing points and lines
	\item \textbf{Intrinsic and extrinsic parameters (definitions, properties)}
	\item Affine camera model (properties)
	\item Camera calibration
	\item \textbf{Epipolar geometry}
	\begin{itemize}
		\item \textbf{Essential matrix }
		\item \textbf{Fundamental matrix}
		\item \textbf{8-pt algorithm}
		\item \textbf{Rectification: what is it? why is it useful?}
	\end{itemize}
	\item Stereo vision
	\begin{itemize}	
		\item Correspondence
		\item \textbf{Reconstruction}
	\end{itemize}
	\item Structure from motion
	\begin{itemize}
		\item \textbf{Factorization algorithm}
	\end{itemize}
\end{itemize}

\section{Technical tools}
\begin{itemize}
	\item \textbf{Least square, langrangian, system of linear equations (Ax=b)}
	\item Linear algebra
	\item Classification: boosting
	\item Unsupervised clustering
	\begin{itemize}
		\item \textbf{EM}
		\item \textbf{Eigensolutions}
	\end{itemize}	
	\item Correlation, convolution
	\item Image normalization
\end{itemize}


\end{document}