CN103366362B - A kind of mine belt image segmentation based on firefly optimized algorithm - Google Patents
A kind of mine belt image segmentation based on firefly optimized algorithm Download PDFInfo
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Abstract
The invention discloses a kind of mine belt image segmentation based on firefly optimized algorithm, belong to technical field of image processing, first pre-service is carried out to mine belt image, colored mine belt image is converted to gray level image and self-adaptation low-pass filtering treatment; Then firefly is evenly distributed in the intensity histogram map space of mine belt image, and the value of each firefly luciferin is upgraded, according to local message, global information and the strategy with iterations adaptive updates step-length, firefly is moved, upgrade the local decision territory radius of firefly, calculate fitness function, according to fitness function search globally optimal solution, after successive ignition, global optimum position is optimal threshold; According to optimal threshold to mine belt Image Segmentation Using, the present invention, in the moving process of firefly, adds global information and the strategy with iterations adaptive updates step-length, and convergence of algorithm speed is fast and convergence precision is high, global optimizing ability is strong, is suitable for mine belt Iamge Segmentation.
Description
Technical field
The invention belongs to technical field of image processing, be specifically related to a kind of firefly optimized algorithm that utilizes to the method for mine belt Image Segmentation Using.
Background technology
At present, in the Mineral Processing Industry of China, major part all uses artificial method to split mine belt, the shortcoming that it has poor real, lavishes labor on, mineral recovery rate is low.
Based on digital image processing techniques mine belt segmentation can not need artificial intervention, in real time mine belt is split, in whole process, Iamge Segmentation be a committed step it can divide band by mine belt.Image segmentation algorithm has a variety of, and because different mine belts has certain difference and mine belt image to need Real-time segmentation in color and gray scale, the image segmentation therefore based on threshold value is applicable to mine belt segmentation.
Based in the image segmentation algorithm of threshold value, based on the thresholding method of intelligent group optimized algorithm for traditional Threshold Segmentation Algorithm, there is certain advantage.Firefly optimized algorithm is a kind of new intelligent group optimized algorithm, but basic firefly optimized algorithm exists the shortcoming that late convergence is slow and convergence precision is low.
Therefore, basic firefly optimized algorithm is improved, and the segmentation being applied to mine belt image is very important.
Summary of the invention
The present invention seeks to overcome artificial segmentation mine belt existing problems, propose a kind of image segmentation based on firefly optimized algorithm to the method for mine belt Image Segmentation Using, the over-segmentation problem of mine belt Iamge Segmentation generation is applied to for basic firefly optimized algorithm, propose a kind of firefly optimized algorithm of improvement, be conducive to the process of mine belt Iamge Segmentation.
In order to achieve the above object, first the present invention carries out greyscale transformation to mine belt image, in order to noise decrease is on the impact of image segmentation, carries out self-adaptation low-pass filtering to gray level image; Carry out initialization to firefly, firefly searches for global optimum using maximum between-cluster variance as fitness function; Each firefly upgrades the position of oneself and the value of fluorescein according to local message and global optimum.By successive ignition, the threshold value making fitness function reach global optimum is the optimal threshold of mine belt Iamge Segmentation.This algorithm is in the process of search global optimum, and firefly not only make use of local message, and make use of global information, and utilizes adaptive step to carry out location updating, and the global optimization ability of algorithm is stronger.
Location updating formula and the adaptive step update strategy of the firefly optimized algorithm after improvement are as follows:
Wherein
for firefly
?
the position in moment,
with
the random function in [0,1] scope,
for firefly exists
the step-length in moment,
represent and ask firefly
with
between Euclidean distance,
for the firefly that fitness in whole firefly group is maximum.
Wherein,
with
be respectively maximal value and the minimum value of step-length,
for maximum iteration time.
The concrete grammar and the step that realize technical solution of the present invention are as follows:
(1) captured in real-time mine belt image in ore dressing process, then inputs mine belt image in a computer, carries out pre-service to mine belt image, and pre-service comprises and image is converted to gray level image and utilizes self-adaptation low-pass filtering to carry out filtering process to image;
(2) initialization of firefly: parameters, arranges maximum iteration time
with firefly number N, and utilize equally distributed random function between (0,1) to produce N number of firefly, initialization is carried out to the position of firefly, makes firefly be evenly distributed in the intensity histogram map space of mine belt image;
(3) upgrade the value of each firefly luciferin, more new formula is
, in formula:
for fluorescein value,
for fluorescein disappearance rate,
for fluorescein turnover rate,
for iterations,
for the fitness function value of mine belt image;
(4) movement of firefly, namely calculate firefly move after position
In firefly optimized algorithm, each firefly is by constantly mobile, and find optimal value, therefore the moving process of firefly is extremely important, and in basic firefly optimized algorithm, step-length is a fixing value.If step-length arranges too little, speed of convergence can be caused excessively slow; If step-length arranges excessive, firefly may skip optimum solution the phase after convergence.In order to ensure convergence of algorithm speed and precision, introduce the strategy upgraded with iterations adaptive updates step-length in the present invention.The moving direction of firefly also can affect convergence, and in order to improve the ability of firefly optimized algorithm global optimizing, the present invention adds global information in the mobile formula of firefly;
A () finds the neighborhood of each firefly, and calculate movement probability
, in formula:
for firefly
to firefly
movement probability,
it is firefly
?
the neighborhood in moment,
what represent is firefly
with
between Euclidean distance, firefly
according to probability
a firefly is selected in its neighborhood
, and move to it;
B () utilizes step size computation formula
according to iterations t, adaptive step is upgraded, according to location updating formula
the position of firefly is upgraded, in computing formula:
for step-length,
for the maximal value of step-length,
for the minimum value of step-length,
with
for the random function in [0,1] scope,
for the firefly that fitness in whole firefly group is maximum;
(5) the is calculated
the dynamic decision territory of individual firefly, upgrades firefly
dynamic decision territory, computing formula is:
, in formula:
be
the dynamic decision territory of individual firefly,
for perception territory radius,
for the turnover rate in dynamic decision territory, be a constant,
for neighborhood number threshold value, for controlling the quantity of neighborhood, the dynamic decision territory calculated in step (5) is used to the calculating of movement probability in next iteration step (4);
(6) position after moving according to the firefly calculated in step (4), calculate the fitness function of mine belt image, computing formula is:
, in formula:
for the inter-class variance of mine belt image,
for the threshold value of mine belt image,
for cumulative probability,
for average;
(7) circulation step (3), (4), (5), (6)
secondary, the position at global optimum place is optimal threshold, and mine belt gray level image is made up of pixel, and each pixel has certain threshold value, therefore can carry out Threshold segmentation according to the optimal threshold obtained to mine belt image, obtain final mine belt image segmentation result.
Parameter in step described in the present invention (2), the maximum iteration time of employing
scope is [10,30], and the scope of firefly number N is [50,100].
The strategy that what step-length adopted in step described in the present invention (4) is with iterations adaptive change, the maximal value of step-length
be 1, the minimum value of step-length
be 0.001.
The present invention compared with prior art has following advantages:
1, the present invention proposes the mine belt image segmentation based on firefly optimized algorithm, effectively can solve the problem of artificial segmentation mine belt, improve the real-time of mine belt segmentation, reduce labour, improve the recovery of mineral, utilize mineral resources efficiently;
2, the firefly optimized algorithm of the present invention's proposition, global information and the step-length update strategy along with iterations adaptive change is introduced in the moving process of firefly, improve speed and the precision of algorithm global optimizing, the optimal threshold of mine belt Iamge Segmentation can be searched sooner and more accurately, decrease the iterations required for the optimal threshold finding mine belt Iamge Segmentation.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the mine belt Iamge Segmentation that the present invention is based on firefly optimized algorithm.
Embodiment
Below in conjunction with drawings and Examples, the present invention is described in further detail, but scope is not limited to described content, in the present embodiment method if no special instructions be conventional method.
Embodiment 1: see Fig. 1, for the tin ore image taken from Xi Ye group great Tun ore dressing plant, Yunnan, utilize the concentrate and tailings mine belt of VC++ software to tin ore to split, method and the concrete steps of employing are as follows:
(1) pre-service of mine belt image, due to mine belt image captured in real-time in ore dressing process, image is easily subject to the impact of outside noise, therefore in this step, first colored mine belt image is converted to gray level image; And then utilize self-adaptation low-pass filtering to carry out filtering process to gray level image;
(2) initialization of firefly, parameters: maximum iteration time
be 10, firefly number N is 50, dynamic decision territory
initial value is 3, perception territory radius
be 5, fluorescein turnover rate
be 0.6, fluorescein disappearance rate
be 0.4, the maximal value of step-length
be 1, the minimum value of step-length
be 0.001; Utilize equally distributed random function between (0,1) to produce 50 fireflies, make firefly be evenly distributed in the intensity histogram map space of mine belt image;
(3) more new formula is utilized
upgrade the value of each firefly luciferin, when iterations is 10 times, the value of 50 firefly luciferins is respectively
(4) movement of firefly, in this step, first each firefly determines its neighborhood, calculates movement probability, determine its moving direction in neighborhood according to movement probability, then move in conjunction with the position of the maximum firefly of fitness in whole firefly group.The moving process of firefly can be summarized as follows:
A () first each firefly determines its neighborhood, and according to
calculate movement probability, in formula:
for firefly
to firefly
movement probability,
it is firefly
?
the neighborhood in moment,
what represent is firefly
with
between Euclidean distance, firefly
according to probability
a firefly is selected in its neighborhood
, and move to it;
B () is according to step size computation formula
adaptive updates is carried out to step-length, according to location updating formula
the position of firefly is upgraded, in formula:
for step-length,
for maximal value=1 of step-length,
for minimum value=0.01 of step-length,
with
for the random function in [0,1] scope,
for the firefly that fitness in whole firefly group is maximum;
(5) the is calculated
the dynamic decision territory of individual firefly, computing formula is:
, in formula:
be
the dynamic decision territory of individual firefly,
for perception territory radius,
be 0.08,
be 5, for controlling the quantity of neighborhood;
(6) position after moving according to the firefly calculated in step (4), utilizes maximum between-cluster variance
, calculate the fitness function of mine belt image; When iterations is 10 times,
(7) by iterative step (3), (4), (5), (6) 10 times, continuous search fitness function maximal value, the optimal threshold searching out mine belt image is 127, mine belt gray level image is made up of pixel, each pixel has certain threshold value, therefore according to optimal threshold, Threshold segmentation is carried out to mine belt image, thus tin ore mine belt is divided into concentrate and tailings.
Embodiment 2: see Fig. 1, utilize the concentrate and tailings mine belt of VC++ software to tin ore to split, the method for employing is identical with embodiment 1 with step, and wherein the number of firefly is 70:
(1) pre-service of mine belt image, due to mine belt image captured in real-time in ore dressing process, image is easily subject to the impact of outside noise, therefore in this step, first colored mine belt image is converted to gray level image; And then utilize self-adaptation low-pass filtering to carry out filtering process to gray level image;
(2) initialization of firefly, parameters: maximum iteration time
be 20, firefly number N is 70, dynamic decision territory
initial value is 3, perception territory radius
be 5, fluorescein turnover rate
be 0.6, fluorescein disappearance rate
be 0.4, the maximal value of step-length
be 1, the minimum value of step-length
be 0.001; Utilize equally distributed random function between (0,1) to produce 70 fireflies, make firefly be evenly distributed in the intensity histogram map space of mine belt image;
(3) more new formula is utilized
upgrade the value of each firefly luciferin, when iterations is 20 times, the fluorescein value of 70 fireflies is respectively
(4) movement of firefly, in this step, first each firefly determines its neighborhood, calculates movement probability, determine its moving direction in neighborhood according to movement probability, then move in conjunction with the position of the maximum firefly of fitness in whole firefly group.The moving process of firefly can be summarized as follows:
A () first each firefly determines its neighborhood, and according to
calculate movement probability, in formula:
for firefly
to firefly
movement probability,
it is firefly
?
the neighborhood in moment,
what represent is firefly
with
between Euclidean distance, firefly
according to probability
a firefly is selected in its neighborhood
, and move to it;
B () is according to step size computation formula
adaptive updates is carried out to step-length, according to location updating formula
the position of firefly is upgraded, in formula:
for step-length,
for maximal value=1 of step-length,
for minimum value=0.01 of step-length,
with
for the random function in [0,1] scope,
for the firefly that fitness in whole firefly group is maximum;
(5) the is calculated
the dynamic decision territory of individual firefly, computing formula is:
, in formula:
be
the dynamic decision territory of individual firefly,
for perception territory radius,
be 0.08,
be 5, for controlling the quantity of neighborhood;
(6) position after moving according to the firefly calculated in step (4), utilizes maximum between-cluster variance
, calculate the fitness function of mine belt image; When iterations is 20 times,
(7) by iterative step (3), (4), (5), (6) 10 times, continuous search fitness function maximal value, the optimal threshold searching out mine belt image is 127, mine belt gray level image is made up of pixel, each pixel has different threshold values, therefore according to optimal threshold, Threshold segmentation is carried out to mine belt image, thus tin ore mine belt is divided into concentrate and tailings.
Embodiment 3: see Fig. 1, split the concentrate of tin ore, chats and mine tailing mine belt, the method for employing is identical with embodiment 1 with step, wherein utilizes maximum between-cluster variance
, calculate fitness function, by iterative step (3), (4), (5), (6) 20 times, constantly search fitness function maximal value, the optimal threshold searching out mine belt image is
, mine belt gray level image is made up of pixel, and each pixel has certain threshold value, therefore carries out Threshold segmentation according to optimal threshold to mine belt image, thus tin ore mine belt is divided into concentrate, chats and mine tailing.
When iterations is 20 times, the value of 50 firefly luciferins is respectively:
Utilize maximum between-cluster variance
, calculate mine belt
The fitness function of image; When iterations is 20 times,
.
Embodiment 4: in order to verify the validity of new firefly optimized algorithm, four width mine belt images have been selected in the present embodiment, basic glowworm swarm algorithm and glowworm swarm algorithm of the present invention is utilized to split it respectively, the parameter of two kinds of firefly optimized algorithm employings is consistent with the parameter in embodiment 1, the glowworm swarm algorithm proposed in the present invention introduces global information and the step-length update strategy along with iterations adaptive change in the moving process of firefly, improve speed and the precision of algorithm global optimizing, the optimal threshold of mine belt Iamge Segmentation can be searched sooner and more accurately, decrease the iterations required for the optimal threshold finding mine belt Iamge Segmentation.
Table 1: experiment comparative result
Claims (3)
1., based on a mine belt image segmentation for firefly optimized algorithm, comprise the following steps:
(1) captured in real-time mine belt image in ore dressing process, carries out pre-service to mine belt image, mine belt image is converted to gray level image, then utilizes self-adaptation low-pass filtering to carry out filtering process to image;
(2) firefly initialization: parameters, arranges maximum iteration time
with firefly number N, and utilize in the intensity histogram map space of mine belt image after equally distributed random function makes N number of firefly be evenly distributed in pre-service between (0,1);
(3) upgrade the value of each firefly luciferin, more new formula is
,
In formula:
for fluorescein value,
for fluorescein disappearance rate,
for fluorescein turnover rate,
for iterations,
for the fitness function value of mine belt image;
(4) calculate firefly move after position
A () finds the neighborhood of each firefly, and calculate movement probability
, in formula:
for firefly
to firefly
the probability of movement,
it is firefly
?
the neighborhood in moment,
what represent is firefly
with
between Euclidean distance, firefly
according to the movement probability calculated
the firefly that a movement probability is maximum is selected in its neighborhood
, and move to it;
B () utilizes step size computation formula
according to t, adaptive updates is carried out to step-length, finally according to location updating formula
the position of firefly is upgraded, in computing formula:
for step-length,
for the maximal value of step-length,
for the minimum value of step-length,
with
for the random function in [0,1] scope,
for the firefly that fitness in whole firefly group is maximum;
(5) the is calculated
the dynamic decision territory of individual firefly, computing formula is:
, in formula:
be
the dynamic decision territory of individual firefly,
for perception territory radius,
for the turnover rate in dynamic decision territory, be a constant,
for neighborhood number threshold value, for controlling the quantity of neighborhood, the dynamic decision territory calculated in step (5) is used to the calculating of movement probability in next iteration step (4);
(6) position after moving according to the firefly calculated in step (4), calculate the fitness function of mine belt image, computing formula is:
, in formula:
for the inter-class variance of mine belt image,
for the threshold value of mine belt image,
for cumulative probability,
for average;
(7) circulation step (3), (4), (5), (6)
secondary, the position at global optimum place is optimal threshold, and mine belt gray level image is made up of pixel, and each pixel has certain threshold value, therefore can carry out Threshold segmentation according to the optimal threshold obtained to mine belt image, obtain final mine belt image segmentation result.
2. the mine belt image segmentation based on firefly optimized algorithm according to claim 1, is characterized in that: maximum iteration time
scope is [10,30], and the scope of firefly number N is [50,100].
3. the mine belt image segmentation based on firefly optimized algorithm according to claim 1, is characterized in that: the maximal value of step-length in step (4)
be 1, the minimum value of step-length
be 0.001.
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| CN104021393A (en) * | 2014-03-31 | 2014-09-03 | 河海大学 | Hyperspectral remote sensing image waveband selection method based on firefly optimization |
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| CN107230213A (en) * | 2017-05-15 | 2017-10-03 | 昆明理工大学 | A kind of colored mine belt zoning map of multi thresholds shaking table based on improvement glowworm swarm algorithm is as split plot design |
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| CN107516318A (en) * | 2017-08-25 | 2017-12-26 | 四川长虹电器股份有限公司 | Multi-Level Threshold Image Segmentation method based on pattern search algorithm and glowworm swarm algorithm |
| CN107590815A (en) * | 2017-09-07 | 2018-01-16 | 陕西师范大学 | Multi-Level Threshold Image Segmentation method based on firefly group's optimization |
| CN108645505B (en) * | 2018-03-21 | 2020-09-18 | 南京信息工程大学 | Stochastic resonance weak signal detection method |
| CN109146053A (en) * | 2018-08-30 | 2019-01-04 | 广西民族大学 | A kind of firefly method for simulating biological ideal free distribution model |
| CN109508662A (en) * | 2018-11-01 | 2019-03-22 | 上海海事大学 | A kind of mining electric locomotive pedestrains safety monitoring method and system |
| CN109919294A (en) * | 2019-02-28 | 2019-06-21 | 湖北工业大学 | A kind of image enchancing method of glowworm swarm algorithm and cuckoo searching algorithm Parallel Fusion |
| CN110909158B (en) * | 2019-07-05 | 2022-10-18 | 重庆信科设计有限公司 | Text classification method based on improved firefly algorithm and K nearest neighbor |
| CN111862135A (en) * | 2020-08-03 | 2020-10-30 | 湖南大奇智能科技有限公司 | Shaking table ore belt image segmentation method |
| CN112463291B (en) * | 2020-11-12 | 2023-01-10 | 苏州浪潮智能科技有限公司 | Virtual machine deployment method, device, equipment and readable storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101923715A (en) * | 2010-09-02 | 2010-12-22 | 西安电子科技大学 | Image segmentation method based on texture information constrained clustering of particle swarm optimization space |
| CN102932870A (en) * | 2012-10-30 | 2013-02-13 | 河南科技大学 | Deployment method of network nodes of wireless sensor |
-
2013
- 2013-04-17 CN CN201310133323.3A patent/CN103366362B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101923715A (en) * | 2010-09-02 | 2010-12-22 | 西安电子科技大学 | Image segmentation method based on texture information constrained clustering of particle swarm optimization space |
| CN102932870A (en) * | 2012-10-30 | 2013-02-13 | 河南科技大学 | Deployment method of network nodes of wireless sensor |
Non-Patent Citations (2)
| Title |
|---|
| Glowworm Swarm Optimization Algorithm Based on Hierarchical Multi-subgroups;Lifang He,etal;《Journal of Information & Computational Science》;20130301;1245-1251 * |
| 一种改进的变步长自适应GSO算法;黄凯 等;《计算机工程》;20120220;185-187,193 * |
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