but rarely used in the fieldof food

but rarely used in the field
of food processing. Among them, the classical Chan–Vese (CV)
model was found to be capable of quickly processing images of
intensity homogeneity without using the gradient information
(Chan and Vese, 2001), while it wasn’t satisfactory for inhomogeneity
ones. On the other hand, the local binary fitting energy
(LBF) model was found to be able to segment the images with
intensity inhomogeneity (Li et al., 2007), while it was expensive
in terms of computation cost due to evolving iterations. In this
paper, we first propose a novel LBF model (NLBF) by adding a contrast
item in LBF and then an adaptive balanced level set evolution
(ABLSE) approach is proposed for extracting chicken area in the
changing process with an aim to use all the advantages of the previous
two approaches. Theoretically, the proposed energy function
mainly consists of the CV energy term and the NLBF energy term as
given below.