  This paper presents an investigation of the use of interest point detection
algorithms from image processing applied to reconstruction of interactions in
high granularity tracking detectors. Their purpose is to extract keypoints from
the data as input to higher level reconstruction algorithms, replacing the role
of human operators in event selection and reconstruction guidance. Simulations
of nu_mu charged current events in a small liquid argon time projection chamber
are used as a concrete example of a modern high granularity tracking detector.
Data from the simulations are used to characterize the localization of interest
points to physical features and the efficiency of finding interest points
associated with the primary vertex and track ends is measured. A high degree of
localization is found, with 93% of detected interest points found within 5mm of
a physical feature. Working in two 2D projections, the primary vertex and both
track ends are found in both projections in 85% of events. It is also shown
that delta electrons can be detected.
