dc.description.abstract |
In this thesis, I propose a novel method for estimating motions between image contours.
The method is fast and can handle both small and large displacements. It uses the distance
transform and its gradients to estimate correspondences between points. The distance
transform of a binary contour represents the distance of each pixel to the nearest contour
pixel. The gradient of the distance transform points in the direction normal to the contour.
By combining unit vectors obtained from the gradient with the original distance transform,
the method produces vectors that correspond to the normal displacement between pairs of
contours. This method can then be extended to compute true motion near corners as well
as parameterized motion models.
Experiments on various shape contours show the method's efficacy in computing normal
displacements. Cases that do not correspond to normal motion are analyzed and corrected
through the use of contour normals and motion models. The method is also applied to gait
recognition, the goal of which is to identify people in videos based on their unique walking
pattern. Many gait recognition methods operate on sequences of binary silhouettes. Since
contours are easily obtainable from silhouettes, the proposed method is well-suited to the
task. I describe two representations, the Histogram of Motion and the Edge Motion Vector, that allow for the comparison of contour motion between frames and sequences. These
representations are tested on a large gait recognition database and achieve rank 1 and rank
5 performance that is comparable to the state of the art. The success of the method on
gait recognition shows it is useful as a standalone representation, but it can also be used to
improve other techniques and for other applications - this is left to future work. |
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