Title :
An Improved Algorithm of the Maximum Entropy Image Segmentation
Author :
Yan He ; Liu Jie ; Yang Dehong ; Wang Pu
Author_Institution :
Coll. of Comput. Sci., Chongqing Univ. of Technol., Chongqing, China
Abstract :
For improving the accuracy of the traditional maximum entropy threshold segmentation algorithm, an improved maximum entropy segmentation algorithm is proposed. Firstly, it determines the possible range of an optimal segmentation threshold according to a simple statistical method, so as to reduce the interference of the background and magnify the proportion of the target region. Secondly, in a certain range of threshold, does image segmentation according to an optimal segmentation threshold, which is obtained by using maximum entropy principle. Simulation experiments show that the improved algorithm not only can improve accuracy and noise immunity effectively, but also can better keep the details of the target region in comparison with the traditional maximum entropy threshold segmentation algorithm.
Keywords :
image segmentation; maximum entropy methods; statistical analysis; accuracy improvement; background interference reduction; improved maximum entropy segmentation algorithm; maximum entropy image segmentation; maximum entropy principle; maximum entropy threshold segmentation algorithm; noise immunity; optimal segmentation threshold; statistical method; Accuracy; Algorithm design and analysis; Entropy; Image edge detection; Image segmentation; Interference; Noise; Image Segmentation; Arithmetic Mean; Binarization;
Conference_Titel :
Intelligent Systems Design and Engineering Applications (ISDEA), 2014 Fifth International Conference on
Conference_Location :
Hunan
Print_ISBN :
978-1-4799-4262-6
DOI :
10.1109/ISDEA.2014.255