Morphological filtering of multi-level image using entropy

This paper presents new properties of the discrete morphological skeleton representation of binary images, for lossless binary image compression, that is based on these properties. We proposed a morphological recognition algorithm using threshold linear superposition theory to analyze the distribution of randomly spaced and oriented blob shaped particles. We recognized the size and position of randomly shaped particles by using the hit/miss transform. We also illustrate the improvement mathematical morphology makes on the entropy thresholding of small targets.