Logarithmic mathematical morphology: a new framework adaptive to illumination changes
This work addresses illumination adaptation in image processing for pattern recognition, but it is incremental as it builds on existing mathematical morphology and LIP frameworks.
The paper tackles the problem of illumination changes in image processing by introducing logarithmic mathematical morphology operators based on the Logarithmic Image Processing model, which is consistent with human vision. Results show that this approach is more efficient on low-contrasted information than classical methods.
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.