Structure tensor images are obtained by a Gaussian smoothing of the dyadic product of gradient image. These images give at each pixel a n × n symmetric positive definite matrix SPD( n ), representing the local orientation and the edge information. Processing such images requires appropriate algorithms working on the Riemannian manifold on the SPD( n ) matrices. This contribution deals with structure tensor image filtering based on L p geometric averaging. In particular, L 1 center-of-mass (Riemannian median or Fermat-Weber point) and L ∞ center-of-mass (Riemannian circumcenter) can be obtained for structure tensors using recently proposed algorithms. Our contribution in this paper is to study the interest of L 1 and L ∞ Riemannian estimators for structure tensor image processing. In particular, we compare both for two image analysis tasks: (i) structure tensor image denoising; (ii) anomaly detection in structure tensor images.
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