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Automatic image segmentation method of continuous quantum goose group algorithm evolution pulse coupling neural network system parameters

A pulse-coupled neural and continuous quantum technology, which is applied in the fields of image understanding and computer vision pattern recognition, can solve the problems that the scope of application is not wide enough, the optimal parameter optimization target entropy criterion is single, etc.

Inactive Publication Date: 2014-05-28
HARBIN ENG UNIV
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Problems solved by technology

However, the existing PCNN parameter optimization methods have a shortcoming that the optimization target entropy criterion for solving the optimal parameters is single, and the scope of application is not wide enough.

Method used

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  • Automatic image segmentation method of continuous quantum goose group algorithm evolution pulse coupling neural network system parameters
  • Automatic image segmentation method of continuous quantum goose group algorithm evolution pulse coupling neural network system parameters
  • Automatic image segmentation method of continuous quantum goose group algorithm evolution pulse coupling neural network system parameters

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Embodiment

[0122] The population size of particle swarm optimization (PSO), cultural algorithm (CA) and quantum goose swarm algorithm (QGSO) is 20, and the maximum number of iterations is 20 generations. For the parameters of the Quantum Goose Swarm Algorithm, refer to the parameter setting of the patent "A Specific Implementation Example of Image Segmentation". For other parameters of the Particle Swarm Algorithm, refer to Lu Guifu et al. 146) the PSO algorithm in "A PCNN Image Segmentation Algorithm for Automatic Parameter Optimization" published on the Internet; other parameters of the cultural algorithm refer to the master's thesis "Research on PCNN Parameter Calibration Based on Cultural Algorithm".

[0123] Now use a specific example to illustrate the specific steps of using the Quantum Goose Swarm Algorithm for PCNN image segmentation:

[0124] Step1: Set the population size M=20 and the maximum iteration number N=20, and set the weighted value of the combined weighted entropy to ...

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Abstract

The invention belongs to the field of computer vision mode recognition and image understanding and relates to an automatic image segmentation method of continuous quantum goose group algorithm evolution pulse coupling neural network system parameters. The method comprises the steps that a minimum combination weighting entropy model of automatic image segmentation of the evolution pulse coupling neural network system parameters is established; a continuous quantum goose group population space is initialized; a simulation quantum rotating door is used for updating the position of each wild goose; the position of each wild goose corresponds to a pulse coupling neural network system parameter, a pulse coupling neural network system is activated for image segmentation, and a fitness value of a new position of an i wild goose is computed; the history optimal quantum positions and the history optimal positions of all wild geese are updated; whether the maximum iteration algebra is reached is checked; and a pulse coupling neural network model is substituted to carry out segmentation on images and output the images after segmentation. The method has the advantages of being small in computing amount, high in convergence rate and high in optimizing capacity.

Description

technical field [0001] The invention belongs to the fields of computer vision pattern recognition and image comprehension, and relates to an automatic image segmentation method for evolving parameters of a pulse-coupled neural network system using a continuous quantum geese swarm algorithm. technical background [0002] Image segmentation refers to the technology and process of dividing an image into regions with different characteristics and extracting objects of interest. Image processing after segmentation, such as feature extraction and target recognition, all depend on the quality of image segmentation, so image segmentation has always been a research hotspot in the field of computer vision and pattern recognition. [0003] Pulse-coupled neural network (PCNN) is derived from the study of the phenomenon of burst-synchronous oscillation of neurons in the cat's visual cortex, and is well suited for image processing due to its biological background. At present, PCNN has be...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06N3/00
Inventor 高洪元赵茂铮孙研徐从强常亮李晨琬
Owner HARBIN ENG UNIV
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