ACTIVE CONTOURS DRIVEN BY LOCAL GAUSSIAN DISTRIBUTION FITTING ENERGY BASED ON LOCAL ENTROPY
Haijun Wang, Ming Liu · International Journal of Pattern Recognition and Artificial Intelligence · 2013
This paper presents a scheme of improvement on the local Gaussian distribution fitting energy (LGDF) model in terms of robustness to initialization and noise. The LGDF energy is redefined as a weighted energy integral. The weights are defined based on local entropy deriving from a gray level distribution of local image, which enables the proposed model to be robust to the initialization. Experimental results prove that the proposed model is more robustness to noise than the original LGDF model, local binary fitting (LBF) model and local image fitting (LIF) model.