Abstract
Keypoint detection is a basic step in many computer
vision algorithms aimed at recognition of objects, automatic
navigation and analysis of biomedical images. Successful implementation
of higher level image analysis tasks, however, is
conditioned by reliable detection of characteristic image local
regions termed keypoints. A large number of keypoint detection
algorithms has been proposed and verified. In this paper we
discuss the most important keypoint detection algorithms. The
main part of this work is devoted to description of a keypoint
detection algorithm we propose that incorporates depth
information computed from stereovision cameras or other depth
sensing devices. It is shown that filtering out keypoints that
are context dependent, e.g. located at boundaries of objects
can improve the matching performance of the keypoints which
is the basis for object recognition tasks. This improvement is
shown quantitatively by comparing the proposed algorithm to
the widely accepted SIFT keypoint detector algorithm. Our study
is motivated by a development of a system aimed at aiding the
visually impaired in space perception and object identification.
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