TY - JOUR
T1 - A comparison of nature inspired algorithms for multi-threshold image segmentation
AU - Osuna-Enciso, Valentín
AU - Cuevas, Erik
AU - Sossa, Humberto
N1 - Funding Information:
V. Osuna-Enciso thanks CONACYT for the scholarship to finish his doctoral studies. He also thanks the CIC-INP and the UDG for the support to complete his studies. H. Sossa would like to thank CONACYT and SIP-IPN for the economical founding to accomplish this research, under Grants 155014 and 20121311. He also thanks the ICYTDF for the founding under project: Intelligent control of humanoid robots and aerial robots and its applications.
PY - 2013/3
Y1 - 2013/3
N2 - In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined class is labeled according to the selected threshold, giving as a result pixel groups that share visual characteristics in the image. Several methods have been proposed in order to solve threshold selection problems; in this work, it is used the method based on the mixture of Gaussian functions to approximate the 1D histogram of a gray level image and whose parameters are calculated using three nature inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony Optimization and Differential Evolution). Each Gaussian function approximates the histogram, representing a pixel class and therefore a threshold point. Experimental results are shown, comparing in quantitative and qualitative fashion as well as the main advantages and drawbacks of each algorithm, applied to multi-threshold problem.
AB - In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined class is labeled according to the selected threshold, giving as a result pixel groups that share visual characteristics in the image. Several methods have been proposed in order to solve threshold selection problems; in this work, it is used the method based on the mixture of Gaussian functions to approximate the 1D histogram of a gray level image and whose parameters are calculated using three nature inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony Optimization and Differential Evolution). Each Gaussian function approximates the histogram, representing a pixel class and therefore a threshold point. Experimental results are shown, comparing in quantitative and qualitative fashion as well as the main advantages and drawbacks of each algorithm, applied to multi-threshold problem.
KW - Artificial Bee Colony Optimization
KW - Automatic thresholding
KW - Differential Evolution
KW - Gaussian function sum
KW - Image segmentation
KW - Intelligent image processing
KW - Particle Swarm Optimization
UR - http://www.scopus.com/inward/record.url?scp=84870250648&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2012.08.017
DO - 10.1016/j.eswa.2012.08.017
M3 - Artículo
SN - 0957-4174
VL - 40
SP - 1213
EP - 1219
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 4
ER -