Region Growing Structuring Elements and New Operators Based on Their Shape

Vincent Morard (1) , Etienne Decencière (1) , Petr Dokládal (1)

1 CMM - Centre de Morphologie Mathématique

This paper proposes new adaptive structuring elements in the framework of mathematical morphology. These structuring elements (SEs) have a fixed size but they adapt their shape to the image content by choosing, recursively, similar pixels in gray-scale, with regard to the seed pixel. These new SEs are called region growing structuring elements (REGSEs). Then, we introduce an original method to obtain some features by analyzing the shape of each REGSE. We get a powerful set of operators, which is able to enhance efficiently thin structures in an image. We illustrate the performance of the proposed filters with an application: the detection of cracks in the framework of non-destructive testing. We compare these methods with others, including morphological amoebas and general adaptive neighborhood structuring elements and we see that these operators, based on REGSE, yield the best detection for our application.

Mots clés: Image Enhancement; Pattern Recognition; Mathematical Morphology; Region Growing Structuring Elements; Crack Detection; Inertia Operator

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Vincent Morard, Etienne Decencière, Petr Dokládal. Region Growing Structuring Elements and New Operators Based on Their Shape. Signal and Image Processing (SIP 2011), Dec 2011, Dallas, United States. pp.Pattern recognition, track 759-018. ⟨hal-00834502⟩