deletions | additions       diff --git a/chapter_Background_and_Related_Work__.tex b/chapter_Background_and_Related_Work__.tex  index 6968558..c5d0dd1 100644  --- a/chapter_Background_and_Related_Work__.tex  +++ b/chapter_Background_and_Related_Work__.tex 
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There is no direct way to modify the time complexity of the algorithm.  % TODO: which errors have been achieved  \subsection{Homography Determination \& Keypoint Matching}  \label{sec:keypointmatching}  A standard approach for estimating camera pose is detecting and  describing keypoints of the current view and a reference image, using  algorithms such as \textsc{Sift}~\cite{lowe1999object}, followed by finding a homography between both keypoint sets. A  keypoint is a salient image location that is invariant to different  viewing angles and scaling. Keypoints are described by a feature vector. By finding a homography, that is a perspective transformation between the keypoints of the current view and a reference image, the current view can be located in the reference image. The $3 \times 3$  homography matrix ($H$) is based on at least four keypoint matches  between both images. However, usually more points are available,  leading to an overdetermined equation. An initial homography matrix is  then created using a least-squares approach and further refined by  various algorithms.  % TODO:  % Where has the approach been used and is suitable for the proposed algorithm.