This paper presents a new method for thresholding
using an evolutionary algorithm for optimizing image threshold
especially for object segmentation. This method is resistant versus
harsh histograms because of suitable fitness function selecting that
consists of the histogram details. Afterwards imperialist
competitive algorithm is recomanded as an evolutionary method
for minimizing the error function. Finally, a powerful algorithm for
image thresholding is founded. The comparisons and experimental
results show that this system is more efficiently toward other
methods; particularly Otsu’s and even former evolutionary
algorithms like GA.
