Tent mapping improved bee colony algorithm for image threshold segmentation

A technology of threshold segmentation and bee colony algorithm, applied in image analysis, image data processing, calculation and other directions, can solve the problems of slow search speed and inconsistent coding, and achieve the effect of fast search speed

Inactive Publication Date: 2015-09-02
NORTHEAST GASOLINEEUM UNIV
View PDF0 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide an improved bee colony algorithm based on Tent mapping for image threshold segmentation, which solves the problems that existing algorithms are prone to premature convergence, non-uniform coding, and slow search speed

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Tent mapping improved bee colony algorithm for image threshold segmentation
  • Tent mapping improved bee colony algorithm for image threshold segmentation
  • Tent mapping improved bee colony algorithm for image threshold segmentation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] The present invention will be described in detail below in combination with specific embodiments.

[0037] The invention proposes an improved artificial bee colony algorithm by utilizing the mechanism that bees search for honey sources in the basic bee colony algorithm.

[0038] An Improved Bee Colony Algorithm for Tent Mapping

[0039] 1.1 Tent mapping, using Tent mapping to initialize the individual of the algorithm;

[0040] Chaos optimization is a relatively new optimization algorithm. Logistic and Tent are commonly used chaotic sequences in this type of algorithm. The common feature of this type of sequence is that it has initial value sensitivity. So use this feature to initialize the algorithm. Logistic can be described by the following formula:

[0041] z k+1 =μz k (1-z k ); (1)

[0042] The expression form of Tent mapping is:

[0043] z k + 1 = ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The object of the invention is to provide a Tent mapping improved bee colony algorithm for image threshold segmentation. The method includes: employing Tent mapping to initialize the individuals of the algorithm; employing the Tent mapping to uniformly distribute initial values in a solution space; employing the standard manual bee colony algorithm to search a new nectar source near a nectar source; updating the position of the new nectar source, maintaining the position of the new nectar source if the position is better than the position of the original nectar source, and otherwise maintaining the position of the original nectar source; updating the position of the new nectar source aiming to a following bee according to the method of step 3 and the selection probability of roulette, maintaining the position of the new nectar source if the position is better than the position of the original nectar source, and otherwise maintaining the position of the original nectar source; updating the optimal solution aiming to a leading bee and the following bee; re-generating the nectar source individual if the optimal solution reaches the limited number; transferring to the step 3 for iteration if the iteration frequency is lower than the preset iteration frequency; and otherwise outputting the optimal solution. The beneficial effects of the Tent mapping improved bee colony algorithm are that premature convergence is prevented, and the searching speed is fast.

Description

technical field [0001] The invention belongs to the technical field of target detection algorithms and relates to an improved bee colony algorithm for Tent mapping of image threshold segmentation. Background technique [0002] Image threshold segmentation is a key technology in object detection, and it is widely used because of its high efficiency and easy implementation. A large number of threshold selection methods have been proposed, which are based on one-dimensional histogram or two-dimensional histogram and its area division method [1] , combined with intelligent algorithms to find the optimal threshold under different criteria [2] , and achieved good application results in different application fields. Artificial Bee Colony Algorithm [1] (Artificial Bee Colony, ABC) is a typical algorithm in this kind of intelligent algorithm. This algorithm is a stochastic optimization algorithm based on the intelligent search behavior of bee swarms proposed by Karaboga in 2005. ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00G06T7/00
Inventor 霍凤财董宏丽任伟建路阳王艳芹康朝海于镝张会珍
Owner NORTHEAST GASOLINEEUM UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap