Abstract
In this paper we propose a novel method for the construction of invariant textural features for grey scale images. The textural features are based on an averaging over the 2D Euclidean transformation group with relational kernels. They are invariant against 2D Euclidean motion and strictly increasing grey scale transformations. Beside other fields of texture analysis applications we consider texture defect detection here. We provide a systematic method how to apply these grey scale features to this task. This will include the localization and classification of the defects. First experiments with real textile texture images taken from the TILDA database show promising results. They are presented in this paper.
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Schael, M. (2001). Texture Defect Detection Using Invariant Textural Features. In: Radig, B., Florczyk, S. (eds) Pattern Recognition. DAGM 2001. Lecture Notes in Computer Science, vol 2191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45404-7_3
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DOI: https://doi.org/10.1007/3-540-45404-7_3
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