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A novel technique for the validation of velocity fields is proposed in this paper. Existing methodologies for vector validation compare each vector of the field with a constant number of neighbours and are as such, unable to detect false vectors in the vicinity of outlier clusters. Moreover, they are apt to over-detect correct vectors as outliers in case of ambiguity due to strong gradients in the velocity field. For this reason, a new validation algorithm has been proposed to enhance robustness in case of outlier clusters and reduce over-detection. This goal is pursued through a coherence-adaptive variable neighbourhood: in contrast with existing methodologies, the novel algorithm automatically enlarges the number of neighbours for a scrutinized vector in order to always ensure a reliable comparison. Coherence is defined as the residual of a vector with a parabolic regression of its closest neighbours and allows a strong enhancement in robustness against outlier clusters also when applied to existing methodologies. In addition to this, the proposed algorithm is provided with a distance-based Gaussian weighting system and the comparison of vectors is performed by means of magnitude and direction instead of vector components. To further improve detection, a new operator to evaluate the median of a sample of data is also suggested and an automatic evaluation of the background error contributes to the adaptivity. Assessment of this novel technique is performed with several Monte Carlo simulations: legitimate velocity fields are contaminated with false vectors collected in groups of different sizes and magnitudes. In order to stress the importance of the numerous innovations introduced in this novel technique, a modified version of previous validation techniques with the coherence-adaptive variable neighbourhood is also proposed as a comparison. An application to a PIV experiment also shows the influence of the validation algorithm in a real image analysis.
|Title of host publication||10th Pacific Symposium on Flow Visualization and Image Processing|
|Publication status||Published - 15 Jun 2015|
Masullo, A., & Theunissen, R. (2015). Improved and robust universal PIV/PTV outlier detection in the presence of clusters. In G. Cardone (Ed.), 10th Pacific Symposium on Flow Visualization and Image Processing