DocumentCode :
3368361
Title :
Maximum entropy image segmentationis based on improved QPSO algorithm
Author :
Chengbo Yu ; Jin Zhang ; Yimeng Zhang
Author_Institution :
Hongqing Univ. of Technol., Chongqing, China
Volume :
7
fYear :
2011
fDate :
12-14 Aug. 2011
Firstpage :
3474
Lastpage :
3477
Abstract :
Image segmentation techniques have great significance in image analysis and recognition, maximum entropy algorithm also has very wide range of applications in image segmentation. In this paper, it is analyzed with the traditional maximum entropy thresholding algorithm, and combined with quantum behavior of particle swarm optimization (QPSO) algorithm, and a new image segmentation algorithm was proposed. This method is the use of maximum entropy image segmentations based on improved (QPSO) algorithm do global search, and make the maximum entropy to be the threshold to for image segmentation. The simulation results show that this method is easy to implement, fast convergence, and have good segmentation.
Keywords :
image recognition; image segmentation; maximum entropy methods; particle swarm optimisation; QPSO algorithm; global search; image analysis; image recognition; image segmentation; image thresholding; maximum entropy algorithm; particle swarm optimization; Algorithm design and analysis; Convergence; Entropy; Image segmentation; Optimization; Particle swarm optimization; Simulation; image segmentation; maximum entropy; quantum behavior of particle swarm optimization (QPSO); regional gray value;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on
Conference_Location :
Harbin, Heilongjiang
Print_ISBN :
978-1-61284-087-1
Type :
conf
DOI :
10.1109/EMEIT.2011.6023830
Filename :
6023830
Link To Document :
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