Adaptive pattern spectrum image description using Euclidean and Geodesic distance without training for texture classification

V. González-Castro; E. Alegre; O. García-Olalla; L. Fernández-Robles; M.T. García-Ordás

Adaptive pattern spectrum image description using Euclidean and Geodesic distance without training for texture classification

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Adaptive pattern spectrum image description using Euclidean and Geodesic distance without training for texture classification

Author(s): V. González-Castro ; E. Alegre ; O. García-Olalla ; L. Fernández-Robles ; M.T. García-Ordás
DOI: 10.1049/iet-cvi.2012.0098

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Author(s): V. González-Castro ¹ ; E. Alegre ¹ ; O. García-Olalla ¹ ; L. Fernández-Robles ² ; M.T. García-Ordás ¹
- Affiliations: 1: Department of Electrical, Systems and Automatics Engineering, University of León, Spain
  2: Department of Mechanical, IT and Aerospace Engineering, University of León, Spain
Source: Volume 6, Issue 6, November 2012, p. 581 – 589
DOI: 10.1049/iet-cvi.2012.0098 , Print ISSN 1751-9632, Online ISSN 1751-9640

Published

Mathematical morphology can be used to extract a shape–size distribution called pattern spectrum (PS) with texture description purposes. However, the structuring element (SE) used to compute it does not vary along the image; and therefore it does not capture its geometrical variations. The author's proposal consists of computing an SE at each pixel whose size and shape varies with two distance criterions: an Geodesic distance and a Euclidean distance, in order to fit the texture as well as possible. Combining the Geodesic and the Euclidean descriptors as just one descriptor, the classification results of several textures from the VisTex and Brodatz database show that this approach outperforms the classical PS, the Geodesic and the Euclidean descriptors separately and, in contrast with other adaptive methods, it does not require previous training.

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Adaptive pattern spectrum image description using Euclidean and Geodesic distance without training for texture classification

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