Logarithmic mathematical morphology: a new framework adaptive to illumination changes

Guillaume Noyel (1, 2, 3)

1 IPRI - International Prevention Research Institute
2 SIGPH@iPRI - University of Strathclyde Institute of Global Public Health
3 University of Strathclyde [Glasgow]

A new set of mathematical morphology (MM) operators adaptive to illumination changes caused by variation of exposure time or light intensity is defined thanks to the Logarithmic Image Processing (LIP) model. This model based on the physics of acquisition is consistent with human vision. The fundamental operators, the logarithmic-dilation and the logarithmic-erosion, are defined with the LIP-addition of a structuring function. The combination of these two adjunct operators gives morphological filters, namely the logarithmic-opening and closing, useful for pattern recognition. The mathematical relation existing between ``classical'' dilation and erosion and their logarithmic-versions is established facilitating their implementation. Results on simulated and real images show that logarithmic-MM is more efficient on low-contrasted information than ``classical'' MM.

Mots clés: illumination changes; Logarithmic Image Processing; Contrast variations; Mathematical Morphology; pattern recognition

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Topologie générale [math.GN]; Analyse numérique [math.NA]; Traitement du signal et de l'image [eess.SP]

Guillaume Noyel. Logarithmic mathematical morphology: a new framework adaptive to illumination changes. CIARP2018 - 23rd Iberoamerican Congress on Pattern Recognition, Nov 2018, Madrid, Spain. , 2018. ⟨hal-01957566⟩