CN105426909A - Coal-rock identification method based on cooperative sparse coding - Google Patents
Coal-rock identification method based on cooperative sparse coding Download PDFInfo
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- CN105426909A CN105426909A CN201510758327.XA CN201510758327A CN105426909A CN 105426909 A CN105426909 A CN 105426909A CN 201510758327 A CN201510758327 A CN 201510758327A CN 105426909 A CN105426909 A CN 105426909A
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- 239000011435 rock Substances 0.000 title claims abstract description 48
- 238000000034 method Methods 0.000 title claims abstract description 34
- 239000003245 coal Substances 0.000 claims abstract description 53
- 238000005457 optimization Methods 0.000 claims description 14
- 230000004044 response Effects 0.000 claims description 9
- 238000009826 distribution Methods 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 6
- 238000005065 mining Methods 0.000 abstract description 4
- 238000003384 imaging method Methods 0.000 abstract description 2
- 239000002699 waste material Substances 0.000 abstract 1
- 238000001514 detection method Methods 0.000 description 5
- 230000014509 gene expression Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 239000000428 dust Substances 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000004186 co-expression Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000005251 gamma ray Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000035939 shock Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2136—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on sparsity criteria, e.g. with an overcomplete basis
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Abstract
The invention discloses a coal-rock identification method based on cooperative sparse coding. The method learns a coal-rock structural unit from coal-rock image data, and the learned structural unit captures the essential structural feature of a coal-rock image, so that the learned structural unit has strong resolving ability and robustness for an imaging environment change, and thus the method has high recognition stability and recognition accuracy, and the reliable coal-rock identification information can be provided for production processes, such as automated mining, automated coal caving and automated waste choicing.
Description
Technical field
The present invention relates to a kind of Coal-rock identification method based on collaborative sparse coding, belong to coal and rock identify field.
Background technology
Namely coal and rock identify automatically identifies coal petrography object by a kind of method is coal or rock.In coal production process, coal and rock identify technology can be widely used in cylinder coal mining, driving, top coal caving, raw coal select the production links such as spoil, for minimizing getting working face operating personnel or realize unmanned operation, alleviate labor strength, improve operating environment, to realize mine safety High-efficient Production significant.
Multiple method is had to be applied to coal and rock identify, as natural Gamma ray detection, radar detection, stress pick, infrared acquisition, active power monitoring, shock detection, sound detection, dust detection, memory cut etc., but there is following problem in these methods: 1. need to install various kinds of sensors obtaining information on existing additional, cause apparatus structure complicated, cost is high.2. the equipment such as coal mining machine roller, development machine in process of production stressed complexity, vibration is violent, serious wear, dust large, sensor deployment is more difficult, and easily cause mechanical component, sensor and electric wiring to be damaged, device reliability is poor.3. for dissimilar plant equipment, there is larger difference in the selection of the best type of sensor and picking up signal point, needs to carry out personalized customization, the bad adaptability of system.
For solving the problem, image technique also more and more comes into one's own and have developed some Coal-rock identification method based on image technique, but existing method is all carry out coal and rock identify with the characteristics of image of human subjective's design or the combination of characteristics of image, the feature of engineer often can not accurately be caught coal petrography image essential structure to cause not have tool robustness to changing the view data change caused because of image-forming condition, thus identifying that stability and recognition correct rate also have very large deficiency.
Need a kind of Coal-rock identification method solving or at least improve one or more problems intrinsic in prior art, to improve coal and rock identify rate and to identify stability.
Summary of the invention
Therefore, the object of the present invention is to provide a kind of Coal-rock identification method based on collaborative sparse coding, the method is from the structural motif of coal petrography view data learning coal petrography, learn the architectural feature that structural motif captures coal petrography image essence, thus there is very strong distinguishing ability and the robustness to imaging circumstances change, thus make the method have very high identification stability and recognition correct rate, the production runes such as cash can be selected to provide reliable coal and rock identify information for automated mining, automatic coal discharge, robotization.
According to a kind of embodiment form, a kind of Coal-rock identification method based on collaborative sparse coding is provided, comprises the steps:
A. the image of a known coal and rock object is respectively gathered;
B. the N number of image block [x of each extraction from coal and rock image
1, x
2... x
n]=X ∈ R
p × N, p is the dimension after image block vectorization;
C. respectively with the coal extracted and rock image block x
iby separating optimization problem
obtain the primitive matrix D of coal and rock image
cand D
r, D
corr=[d
1, d
2... d
k] ∈ R
p × K;
D. by separating optimization problem
obtain coal and rock image block x respectively
iwith D=[D
cd
r] the coefficient u of each image block that expresses
i=[u
i1, u
i2... u
i2K]
t;
E. the primitive response distribution z of coal and rock image is obtained respectively
cand z
r, wherein z
corr=[z
1, z
2... z
2K], a kth primitive response
Wherein,
For normalized parameter;
F. for coal petrography image to be identified, N number of image block y is extracted by the method identical with step B
i, with the D=[D tried to achieve in step C
cd
r] express image block y
i, by separating optimization problem
obtain the coefficient of each image block
G. the primitive response distribution of coal petrography image to be identified is obtained by the method identical with step e
H. use
the similarity of tolerance and known coal and rock object, is coal when being less than 1, otherwise is rock, wherein,
δ is given parameter.
Further specifically but without limitation, the optimization method of step C is:
C1. give D initialize, iterations is set;
C2. fix D, use
Obtain the coefficient U of all image blocks;
C3. fix U, ask
C4.C2 and C3 hockets until iteration terminates.
Accompanying drawing explanation
By following explanation, accompanying drawing embodiment becomes aobvious and sees, its only with at least one described by reference to the accompanying drawings preferably but the way of example of non-limiting example provide.
Fig. 1 is the principle schematic of Coal-rock identification method of the present invention.
Specific embodiments
Fig. 1 is the principle schematic of Coal-rock identification method of the present invention, mainly comprise 3 layers: image layer, coding layer and pond layer, image layer provides input to coding layer, the present embodiment with abstract image block from gray level image as coding layer input, also can from image abstract image characteristic set if sift feature is as input; Expression coefficient when coding layer calculates each image block K primitive co expression learnt respectively from coal petrography view data, according to computing method make to express nonzero element in coefficient seldom, be then called sparse coding, the present embodiment l
1-norm optimizes calculation expression coefficient, and thus the coefficient of gained is sparse; Pond layer calculates the statistical property of all expression coefficients and then obtains the feature representation of input picture, the present embodiment average statistical property, and concrete implementation step is as follows:
A. from the scene of coal and rock identify task as coal-face collection comprises the image of coal and rock, therefrom intercept the image-region only comprising coal and only comprise rock, then unification normalizes to suitable size if 32*32 pixel size is as coal petrography sample image;
B. the N number of image block of each extraction 2N image block [x altogether from two sample images
1, x
2... x
2N]=X ∈ R
p × 2N, p is the dimension after image block vectorization; As tile size gets 6*6 pixel, be that 2 pixels are slided sampled images block in sample image with step-length, standardization carried out to each image block vector: use
remove the average of brightness of image, to eliminate the impact of brightness change, use
image block vector is normalized, wherein, 1
prepresent complete 1 vector of p dimension, η is constant value;
C. respectively with the coal extracted and rock image block x
iby separating optimization problem
obtain the primitive matrix D of coal and rock image
cand D
r, D
corr=[d
1, d
2... d
k] ∈ R
p × K;
Method for solving can adopt the method for alternating minimization D and U, namely follows these steps to process:
C1. give D initialize, iterations is set;
C2. fix D, use
Obtain the sparse coefficient U of all image blocks;
C3. fix U, ask
C4.C2 and C3 hockets until iteration terminates.
The available approximate gradient algorithm of optimization of step C2, the coefficient ui corresponding to each image block xi adopts the following step optimization:
1. give coefficient u initialize, iterations is set;
2. in iteration each time:
U ← u+ ξ D
t(x-Du), ξ is iteration step length;
3. repeat 2 until iteration terminates.
The optimization of step C3 can adopt block coordinate descent algorithm, optimizes with the following step:
1.B←XU
T,C←UU
T
2.Fork=1,2,...,K
3. repeat 2 until convergence.
D. by separating optimization problem
obtain coal and rock image block x respectively
iwith D=[D
cd
r] the coefficient u of each image block that expresses
i=[u
i1, u
i2... u
i2K]
t;
E. the primitive response distribution z of coal and rock image is obtained respectively
cand z
r, wherein z
corr=[z
1, z
2... z
2K], a kth primitive response
Wherein,
For normalized parameter;
F. for coal petrography image to be identified, N number of image block y is extracted by the method identical with step B
i, with the D=[D tried to achieve in step C
cd
r] express image block y
i, by separating optimization problem
obtain the coefficient of each image block
G. the primitive response distribution of coal petrography image to be identified is obtained by the method identical with step e
H. use
the similarity of tolerance and known coal and rock object, is coal when being less than 1, otherwise is rock, wherein,
δ is given parameter.
Claims (2)
1., based on a Coal-rock identification method for collaborative sparse coding, it is characterized in that comprising the following steps:
A. the image of a known coal and rock object is respectively gathered;
B. the N number of image block [x of each extraction from coal and rock image
1, x
2... x
n]=X ∈ R
p × N, p is the dimension after image block vectorization;
C. respectively with the coal extracted and rock image block x
iby separating optimization problem
obtain the primitive matrix D of coal and rock image
cand D
r, D
corr=[d
1, d
2... d
k] ∈ R
p × K;
D. by separating optimization problem
obtain coal and rock image block x respectively
iwith D=[D
cd
r] the coefficient u of each image block that expresses
i=[u
i1, u
i2... u
i2K]
t;
E. the primitive response distribution z of coal and rock image is obtained respectively
cand z
r, wherein z
corr=[z
1, z
2... z
2K], a kth primitive response
Wherein,
For normalized parameter;
F. for coal petrography image to be identified, N number of image block y is extracted by the method identical with step B
i, with the D=[D tried to achieve in step C
cd
r] express image block y
i, by separating optimization problem
obtain the coefficient of each image block
G. the primitive response distribution of coal petrography image to be identified is obtained by the method identical with step e
H. use
the similarity of tolerance and known coal and rock object, is coal when being less than 1, otherwise is rock, wherein,
δ is given parameter.
2. method according to claim 1, is characterized in that the optimization method of step C is:
C1. D initialize is given, setting iterations;
C2. fix D, use
obtain the sparse coefficient U of all image blocks;
C3. fix U, ask
C4.C2 and C3 hockets until iteration terminates.
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Cited By (1)
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CN113505691A (en) * | 2021-07-09 | 2021-10-15 | 中国矿业大学(北京) | Coal rock identification method and identification reliability indication method |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100040296A1 (en) * | 2008-08-15 | 2010-02-18 | Honeywell International Inc. | Apparatus and method for efficient indexing and querying of images in security systems and other systems |
CN104751192A (en) * | 2015-04-24 | 2015-07-01 | 中国矿业大学(北京) | Method for recognizing coal and rock on basis of co-occurrence features of image blocks |
CN104778476A (en) * | 2015-04-10 | 2015-07-15 | 电子科技大学 | Image classification method |
-
2015
- 2015-11-10 CN CN201510758327.XA patent/CN105426909A/en active Pending
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100040296A1 (en) * | 2008-08-15 | 2010-02-18 | Honeywell International Inc. | Apparatus and method for efficient indexing and querying of images in security systems and other systems |
CN104778476A (en) * | 2015-04-10 | 2015-07-15 | 电子科技大学 | Image classification method |
CN104751192A (en) * | 2015-04-24 | 2015-07-01 | 中国矿业大学(北京) | Method for recognizing coal and rock on basis of co-occurrence features of image blocks |
Non-Patent Citations (1)
Title |
---|
张鹏等: "基于稀疏特征的交通流视频检测算法", 《南京大学学报(自然科学)》 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113505691A (en) * | 2021-07-09 | 2021-10-15 | 中国矿业大学(北京) | Coal rock identification method and identification reliability indication method |
CN113505691B (en) * | 2021-07-09 | 2024-03-15 | 中国矿业大学(北京) | Coal rock identification method and identification credibility indication method |
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