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@@ -135,7 +135,7 @@ $$
 
 ##### Approaches 3: Linear approach
 
-$x_1\simeq P_1X$ and $x_2\simeq P_2X$
+$x_1\cong P_1X$ and $x_2\cong P_2X$
 
 $x_1\times P_1X = 0$ and $x_2\times P_2X = 0$
 
diff --git a/pages/CSE559A/CSE559A_L26.md b/pages/CSE559A/CSE559A_L26.md
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+# CSE559A Lecture 26
+
+## Continue on Geometry and Multiple Views
+
+### The Essential and Fundamental Matrices
+
+#### Math of the epipolar constraint: Calibrated case
+
+Recall Epipolar Geometry
+
+![Epipolar Geometry Configuration](https://notenextra.trance-0.com/CSE559A/Epipolar_geometry_setup.png)
+
+Epipolar constraint:
+
+If we set the config for the first camera as the world origin and $[I|0]\begin{pmatrix}y\\1\end{pmatrix}=x$, and $[R|t]\begin{pmatrix}y\\1\end{pmatrix}=x'$, then
+
+Notice that $x'\cdot [t\times (Ry)]=0$
+
+$$
+x'^T E x_1 = 0
+$$
+
+We denote the constraint defined by the Essential Matrix as $E$.
+
+$E x$ is the epipolar line associated with $x$ ($l'=Ex$)
+
+$E^T x'$ is the epipolar line associated with $x'$ ($l=E^T x'$)
+
+$E e=0$ and $E^T e'=0$ ($x$ and $x'$ don't matter)
+
+$E$ is singular (rank 2) and have five degrees of freedom.
+
+#### Epipolar constraint: Uncalibrated case
+
+If the calibration matrices $K$ and $K'$ are unknown, we can write the epipolar constraint in terms of unknown normalized coordinates:
+
+$$
+x'^T_{norm} E x_{norm} = 0
+$$
+
+where $x_{norm}=K^{-1} x$, $x'_{norm}=K'^{-1} x'$
+
+$$
+x'^T_{norm} E x_{norm} = 0\implies x'^T_{norm} Fx=0
+$$
+
+where $F=K'^{-1}EK^{-1}$ is the **Fundamental Matrix**.
+
+$$
+(x',y',1)\begin{bmatrix}
+f_{11} & f_{12} & f_{13} \\
+f_{21} & f_{22} & f_{23} \\
+f_{31} & f_{32} & f_{33}
+\end{bmatrix}\begin{pmatrix}
+x\\y\\1
+\end{pmatrix}=0
+$$
+
+Properties of $F$:
+
+$F x$ is the epipolar line associated with $x$ ($l'=F x$)
+
+$F^T x'$ is the epipolar line associated with $x'$ ($l=F^T x'$)
+
+$F e=0$ and $F^T e'=0$
+
+$F$ is singular (rank two) and has seven degrees of freedom
+
+#### Estimating the fundamental matrix
+
+Given: correspondences $x=(x,y,1)^T$ and $x'=(x',y',1)^T$
+
+Constraint: $x'^T F x=0$
+
+$$
+(x',y',1)\begin{bmatrix}
+f_{11} & f_{12} & f_{13} \\
+f_{21} & f_{22} & f_{23} \\
+f_{31} & f_{32} & f_{33}
+\end{bmatrix}\begin{pmatrix}
+x\\y\\1
+\end{pmatrix}=0
+$$
+
+**Each pair of correspondences gives one equation (one constraint)**
+
+At least 8 pairs of correspondences are needed to solve for the 9 elements of $F$ (The eight point algorithm)
+
+We know $F$ needs to be singular/rank 2. How do we force it to be singular?
+
+Solution: take SVD of the initial estimate and throw out the smallest singular value
+
+$$
+F=U\begin{bmatrix}
+\sigma_1 & 0 \\
+0 & \sigma_2 \\
+0 & 0
+\end{bmatrix}V^T
+$$
+
+## Structure from Motion
+
+Not always uniquely solvable.
+
+If we scale the entire scene by some factor $k$ and, at the same time, scale the camera matrices by the factor of $1/k$, the projections of the scene points remain exactly the same:
+$x\cong PX  =(1/k P)(kX)$
+
+Without a reference measurement, it is impossible to recover the absolute scale of the scene!
+
+In general, if we transform the scene using a transformation $Q$ and apply the inverse transformation to the camera matrices, then the image observations do not change:
+
+$x\cong PX  =(P Q^{-1})(QX)$
+
+### Types of Ambiguities
+
+![Ambiguities in projection](https://notenextra.trance-0.com/CSE559A/Ambiguities_in_projection.png)
+
+### Affine projection : more general than orthographic
+
+A general affine projection is a 3D-to-2D linear mapping plus translation:
+
+$$
+P=\begin{bmatrix}
+a_{11} & a_{12} & a_{13} & t_1 \\
+a_{21} & a_{22} & a_{23} & t_2 \\
+0 & 0 & 0 & 1
+\end{bmatrix}=\begin{bmatrix}
+A & t \\
+0^T & 1
+\end{bmatrix}
+$$
+
+In non-homogeneous coordinates:
+
+$$
+\begin{pmatrix}
+x\\y\\1
+\end{pmatrix}=\begin{bmatrix}
+a_{11} & a_{12} & a_{13} \\
+a_{21} & a_{22} & a_{23}
+\end{bmatrix}\begin{pmatrix}
+X\\Y\\Z
+\end{pmatrix}+\begin{pmatrix}
+t_1\\t_2
+\end{pmatrix}=AX+t
+$$
+
+### Affine Structure from Motion
+
+Given: 𝑚 images of 𝑛 fixed 3D points such that
+
+$$
+x_{ij}=A_iX_j+t_i, \quad i=1,\dots,m, \quad j=1,\dots,n
+$$
+
+Problem: use the 𝑚𝑛 correspondences $x_{ij}$ to estimate 𝑚 projection matrices $A_i$ and translation vectors $t_i$, and 𝑛 points $X_j$
+
+The reconstruction is defined up to an arbitrary affine transformation $Q$ (12 degrees of freedom):
+
+$$
+\begin{bmatrix}
+A & t \\
+0^T & 1
+\end{bmatrix}\rightarrow\begin{bmatrix}
+A & t \\
+0^T & 1
+\end{bmatrix}Q^{-1}, \quad \begin{pmatrix}X_j\\1\end{pmatrix}\rightarrow Q\begin{pmatrix}X_j\\1\end{pmatrix}
+$$
+
+How many constraints and unknowns for $m$ images and $n$ points? 
+
+$2mn$ constraints and $8m + 3n$ unknowns
+
+To be able to solve this problem, we must have $2mn \geq 8m+3n-12$ (affine ambiguity takes away 12 dof)
+
+E.g., for two views, we need four point correspondences
 
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