A new nonlinear technique for filtering motion fields and other multivariate data is introduced. The method is developed from mathematical morphological area openings and uses a vector-to-scalar transform, in which each vector is replaced by the sum of the distances to its connected neighbours, to control the growth of extrema regions. As the filter either perfectly preserves or completely removes image components, it is able to remove noise without altering significant features. In addition, at larger area sizes, a meaningful interpretation of the underlying structure is achieved. Results show that the vector area morphology sieve performs well in comparison to the widely used vector median filter.
|Number of pages||8|
|Journal||IEE Proceedings - Vision Image and Signal Processing|
|Publication status||Published - Aug 2003|