Dense Correspondence Field Estimation from Multiple Images
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Beschreibung
Most optical flow algorithms assume pairs of images
that are acquired with an ideal, short exposure time.
We present two approaches, that use additional images
of a scene to estimate highly accurate, dense correspondence
fields. In our first approach we consider
video sequences that are acquired with alternating exposure
times so that a short-exposure image is followed
by a long-exposure image that exhibits motion-blur.
With the help of the two enframing short-exposure images,
we can decipher not only the motion information
encoded in the long-exposure image, but also estimate
occlusion timings, which are a basis for artifact-free
frame interpolation. In our second approach we consider
the data modality of multi-view video sequences,
as it commonly occurs, e.g., in stereoscopic video. As
several images capture nearly the same data of a scene,
this redundancy can be used to establish more robust
and consistent correspondence fields than the consideration
of two images permits.
Autor*in
Anita Sellent