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\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{1}{section.1}}
\@writefile{toc}{\contentsline {section}{\numberline {2}Configuration}{1}{section.2}}
\@writefile{toc}{\contentsline {section}{\numberline {3}Training}{1}{section.3}}
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Asymmetrical root filter trained with warped positives and random negatives. This is just one of the root filters created for each class, in this case a car.}}{2}{figure.1}}
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\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Mixture of two mirrored root filters trained with latent positives and hard negatives. In this stage there are still multiple models created from the initial positives.}}{2}{figure.2}}
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\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces Part of a final model, only one of the mirrored root filters. On the left the root filters; in the middle the part filters and on the right the cost of placing the part filters away from their initial position, relative to the root.}}{2}{figure.3}}
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\@writefile{toc}{\contentsline {section}{\numberline {4}Detection}{3}{section.4}}
\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces Example of a vehicle detection. The red box denotes the root filter detection while the blue boxes are part filters.}}{3}{figure.4}}
\newlabel{fig:detection}{{4}{3}{Example of a vehicle detection. The red box denotes the root filter detection while the blue boxes are part filters}{figure.4}{}}