Shai Avidan (Tel-Aviv University)
Tuesday, 21.11.2017, 11:30
Co-occurrence Filter (CoF) is a boundary preserving filter. It is based on the
Bilateral Filter (BF) but instead of using a Gaussian on the range values to
preserve edges it relies on a co-occurrence matrix. Pixel values that co-occur
frequently in the image (i.e., inside textured regions) will have a high weight
in the co-occurrence matrix. This, in turn, means that such pixel pairs will be
averaged and hence smoothed, regardless of their intensity differences. On
the other hand, pixel values that rarely co-occur (i.e., across texture
boundaries) will have a low weight in the co-occurrence matrix. As a result,
they will not be averaged and the boundary between them will be preserved.
The CoF therefore extends the BF to deal with boundaries, not just edges. It
learns co-occurrences directly from the image. We can achieve various
filtering results by directing it to learn the co-occurrence matrix from a part of
the image, or a different image. We give the definition of the filter, discuss
how to use it with color images and show several use cases.