Periodicity estimation of nearly regular textures based on discrepancy norm

G. Stübl, P. Haslinger, V. Wieser, J. Scharinger, B. Moser. Periodicity estimation of nearly regular textures based on discrepancy norm. pages 866106 doi:10.1117/12.2002396, 3, 2013.

  • Gernot Stübl
  • Peter Haslinger
  • Volkmar Wieser
  • Josef Scharinger
  • Bernhard Moser
  • P. R. Bingham
  • E. Y. Lam
BuchProceedings SPIE 8661, Image Processing: Machine Vision Applications VI
TypIn Konferenzband
Seiten866106 doi:10.1117/12.2002396
Abstract This paper proposes a novel approach to determine the texture periodicity, the texture element size and further characteristics like the area of the basin of attraction in the case of computing the similarity of a test image patch with a reference. The presented method utilizes the properties of a novel metric, the so-called discrepancy norm. Due to the Lipschitz and the monotonicity property the discrepancy norm distinguishes itself from other metrics by well-formed and stable convergence regions. Both the periodicity and the convergence regions are closely related and have an immediate impact on the performance of a subsequent template matching and evaluation step. The general form of the proposed approach relies on the generation of discrepancy norm induced similarity maps at random positions in the image. By applying standard image processing operations like watershed and blob analysis on the similarity maps a robust estimation of the characteristic periodicity can be computed. From the general approach a tailored version for orthogonal aligned textures is derived which shows robustness to noise disturbed images and is suitable for estimation on near regular textures. In an experimental set-up the estimation performance is tested on samples of standardized image databases and is compared with state-of-theart methods. Results show that the proposed method is applicable to a wide range of nearly regular textures and estimation results keeps up with current methods. When adding a hypothesis generation/selection mechanism it even outperforms the current state-or-the-art.