Homogeneity of a region in the logarithmic image processing framework: application to region growing algorithms
This work addresses region segmentation challenges in image processing, offering incremental improvements to existing techniques.
The paper tackled the problem of evaluating region homogeneity in image processing by introducing two new heterogeneity criteria based on Logarithmic Image Processing (LIP) operators, resulting in improved robustness to contrast variations and reduced chaining effects in region growing algorithms.
The current paper deals with the role played by Logarithmic Image Processing (LIP) operators for evaluating the homogeneity of a region. Two new criteria of heterogeneity are introduced, one based on the LIP addition and the other based on the LIP scalar multiplication. Such tools are able to manage Region Growing algorithms following the Revol's technique: starting from an initial seed, they consist of applying specific dilations to the growing region while its inhomogeneity level does not exceed a certain level. The new approaches we introduce are significantly improving Revol's existing technique by making it robust to contrast variations in images. Such a property strongly reduces the chaining effect arising in region growing processes.