CN108052962A - A kind of Spectral matching algorithm based on improved edit-distance - Google Patents

A kind of Spectral matching algorithm based on improved edit-distance Download PDF

Info

Publication number
CN108052962A
CN108052962A CN201711227706.1A CN201711227706A CN108052962A CN 108052962 A CN108052962 A CN 108052962A CN 201711227706 A CN201711227706 A CN 201711227706A CN 108052962 A CN108052962 A CN 108052962A
Authority
CN
China
Prior art keywords
spectral
distance
pigment
reflectivity
spectral reflectivity
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201711227706.1A
Other languages
Chinese (zh)
Inventor
王可
殷颖
毛力
王伟超
赵丽娟
王蕾蕾
杨蕾
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xian University of Architecture and Technology
Original Assignee
Xian University of Architecture and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xian University of Architecture and Technology filed Critical Xian University of Architecture and Technology
Priority to CN201711227706.1A priority Critical patent/CN108052962A/en
Publication of CN108052962A publication Critical patent/CN108052962A/en
Pending legal-status Critical Current

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/22Matching criteria, e.g. proximity measures
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/18Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mathematical Physics (AREA)
  • Evolutionary Biology (AREA)
  • Pure & Applied Mathematics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Mathematical Optimization (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Operations Research (AREA)
  • Health & Medical Sciences (AREA)
  • Probability & Statistics with Applications (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Algebra (AREA)
  • Artificial Intelligence (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Evolutionary Computation (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)
  • Spectrometry And Color Measurement (AREA)

Abstract

The invention discloses a kind of Spectral matching algorithms based on improved edit-distance, comprise the following steps:1) normal pigment block is made;2) spectral reflectivity of mural painting paint and the spectral reflectivity of normal pigment block are gathered;3) spectral reflectivity of the spectral reflectivity of the mural painting paint obtained using the Spectral matching algorithm of improved edit-distance to step 2) and normal pigment block carries out Spectral matching, the Spectral matching based on improved edit-distance is completed, the Spectral matching result precision of the algorithm is higher.

Description

A kind of Spectral matching algorithm based on improved edit-distance
Technical field
The invention belongs to digital image processing fields, are related to a kind of Spectral matching algorithm based on improved edit-distance.
Background technology
Spectral Matching Technique is exactly by by existing number in measured spectral reflectivity and spectral reflectance data storehouse According to being compared, the technology of the similitude or otherness between two curves is acquired, which can accurately know on the image Other pixel.In the pigment material identification of mural painting, by the spectral reflectivity of unknown pigment in mural painting and normal pigment spectroscopic data The spectral reflectivity of each sample pigment is matched in storehouse, final to obtain one most like with the unknown pigment reflectivity Spectral reflectivity curve.It is considered that the most matched pigment provided in the feature and library of spectra of this unknown pigment is consistent , i.e., the corresponding sample pigment of most like reflectance curve is exactly the recognition result of unknown pigment, thus in mural painting Unknown pigment has carried out more accurate lossless identification, with the fast development of Spectral Matching Technique, has multiple spectrum It is used for solving practical problems with algorithm, such as:Spectroscopic data codes match algorithm, spectrum angle matching algorithm, spectral information dissipate Degree method, spectrum similarity mode algorithm etc..However there is the problem of matching result precision is relatively low in matching algorithm.
The content of the invention
The shortcomings that it is an object of the invention to overcome the above-mentioned prior art, provides a kind of light based on improved edit-distance Matching algorithm is composed, the Spectral matching result precision of the algorithm is higher.
In order to achieve the above objectives, the Spectral matching algorithm of the present invention based on improved edit-distance comprises the following steps:
1) normal pigment block is made;
2) spectral reflectivity of mural painting paint and the spectral reflectivity of normal pigment block are gathered;
3) spectral reflectivity of the mural painting paint obtained using the Spectral matching algorithm of improved edit-distance to step 2) And the spectral reflectivity of normal pigment block carries out Spectral matching, completes the Spectral matching based on improved edit-distance.
The manufacturing process of step 1) Plays pigment cake is:Block pigment is impregnated with clear water, then by the bright of phase homogenous quantities Glue is added in clear water, is then ground with mortar, applies one layer of calcium carbonate on the surface of substrate, grid is depicted after drying, then Fuel after grinding is coated on calcium carbonate, normal pigment block is obtained after drying.
It is anti-by the spectral reflectivity of spectrophotometer acquisition mural painting paint and the spectrum of normal pigment block in step 2) Penetrate rate.
The concrete operations of step 3) are:
δ del (a), δ ins (a) and δ subs (a, b) 3a) is set to represent deletion a, insertion a respectively and replace a corresponding generations with b Valency, then in the case where cost is all for l, cost equation is:
It 3b) sets cost equation C (i, j) and represents and A [1 ... i] is changed into cost needed for B [1 ... j], then cost equation C (i, j) is:
Character string φ there are two 3a) settingmAnd φrn, φm1φ2…φm, φrn1φ2…φrn, construction matching pass It is matrix, matching relationship matrix is m+2 rows, the matrix of n+2 row;
4a) matching relationship matrix is filled according to formula (3);
The element in the matching relationship matrix lower right corner is character string φmWith φrnBetween editing distance ld, i.e., by character string φmChange to character string φrnThe minimum number of Shi Jinhang edit operations;
5a) spectral reflectivity of the spectral reflectivity to mural painting paint and normal pigment block is normalized, by mural painting The amplitude of the spectral reflectivity of paint and the amplitude of the spectral reflectivity of normal pigment block are limited to section [0,1];If i-th The reflectivity of spectrum is M (i), and the corresponding vector of i-th spectrum is S (i), then has
6a) set the decision condition of editing distance as:
Wherein, T is the threshold value adaptively chosen, the amplitude of the spectral reflectivity of mural painting paint and the light of normal pigment block Compose the absolute value of the difference of reflectivity | φirj|={ X1,X2,……,XK, K is data amount check, and the value of K is m*n, XkFor two The difference of reflectivity, φiFor the spectral reflectivity of the normal pigment block of standard, φrjFor the spectral reflectivity of mural painting paint;
The tendency that the curve of spectrum is calculated based on editing distance algorithm 7a) is used, given threshold T is bent by spectrum by threshold value T Line is divided into two groups of different data, one group of two reflectance value unanimous circumstances of expression, and another set represents that two reflected values are inconsistent Situation, set C0In XiTo be less than the difference of threshold value, set C1The X of the insideiTo be more than the difference of threshold value.
C0={ X1,X2,…,XK}Xi≤T (7)
C1={ Xt+1,Xt+2,…,XN}Xi>T (8)
Every XiThe probability of appearance is pi=1/N.
Belong to C0And C1Class occur probability be respectively:
C0Class and C1Class occur average be:
C0Class and C1Class occur variance be:
It is possible thereby to calculate to obtain C0Class and C1The variance within clusters of classInter-class varianceAnd population varianceRespectively:
WhenWhen maximum, the value of corresponding threshold value T is optimal threshold.
The corresponding curve of spectrum of optimal threshold criterion pigment cake spectral reflectivity and mural painting paint spectral reflectivity Whether the corresponding curve of spectrum is a kind of curve of substance.
The invention has the advantages that:
Spectral matching algorithm of the present invention based on improved edit-distance is compiled in concrete operations by using improving The spectral reflectivity for the mural painting paint that the Spectral matching algorithm of volume distance obtains step 2) and the spectrum of normal pigment block are anti- It penetrates rate and carries out Spectral matching, it is easy to operate to improve the precision of Spectral matching result, it is convenient, to realize wall painting pigment substance It accurately identifies.
Specific embodiment
The present invention is described in further detail with reference to embodiment:
Spectral matching algorithm of the present invention based on improved edit-distance comprises the following steps:
1) normal pigment block is made;
2) spectral reflectivity of mural painting paint and the spectral reflectivity of normal pigment block are gathered;
3) spectral reflectivity of the mural painting paint obtained using the Spectral matching algorithm of improved edit-distance to step 2) And the spectral reflectivity of normal pigment block carries out Spectral matching, completes the Spectral matching based on improved edit-distance.
The manufacturing process of step 1) Plays pigment cake is:Block pigment is impregnated with clear water, then by the bright of phase homogenous quantities Glue is added in clear water, is then ground with mortar, applies one layer of calcium carbonate on the surface of substrate, grid is depicted after drying, then Fuel after grinding is coated on calcium carbonate, normal pigment block is obtained after drying.
It is anti-by the spectral reflectivity of spectrophotometer acquisition mural painting paint and the spectrum of normal pigment block in step 2) Penetrate rate.
The concrete operations of step 3) are:
3a) standard of the editing distance between two character strings A and B is defined as:It (including replacing, is inserted into basic operation And delete) character string A is converted into the minimum operation number needed for B, editing distance is defined as with suitable recursion equation Cost function deletes a, insertion a and replaces a corresponding generations with b if δ del (a), δ ins (a) and δ subs (a, b) are represented respectively Valency, then in the case where cost is all for l, cost equation is:
It 3b) sets cost equation C (i, j) and represents and A [1 ... i] is changed into cost needed for B [1 ... j], then cost equation C (i, j) is:
Character string φ there are two 3c) settingmAnd φrn, φm1φ2…φm, φrn1φ2…φrn, construction matching pass It is matrix, matching relationship matrix is m+2 rows, the matrix of n+2 row;
4d) matching relationship matrix is filled according to formula (3);
The element in the matching relationship matrix lower right corner is character string φmWith φrnBetween editing distance ld, i.e., by character string φmChange to character string φrnThe minimum number of Shi Jinhang edit operations;
5e) spectral reflectivity of the spectral reflectivity to mural painting paint and normal pigment block is normalized, by mural painting The amplitude of the spectral reflectivity of paint and the amplitude of the spectral reflectivity of normal pigment block are limited to section [0,1];If i-th The reflectivity of spectrum is M (i), and the corresponding vector of i-th spectrum is S (i), then has
6f) set the decision condition of editing distance as:
Wherein, T is the threshold value adaptively chosen, the amplitude of the spectral reflectivity of mural painting paint and the light of normal pigment block Compose the absolute value of the difference of reflectivity | φirj|={ X1,X2,……,XK, K is data amount check, and the value of K is m*n, XkFor two The difference of reflectivity, φiFor the spectral reflectivity of the normal pigment block of standard, φrjFor the spectral reflectivity of mural painting paint;
The tendency that the curve of spectrum is calculated based on editing distance algorithm 7h) is used, given threshold T is bent by spectrum by threshold value T Line is divided into two groups of different data, one group of two reflectance value unanimous circumstances of expression, and another set represents that two reflected values are inconsistent Situation, set C0In XiTo be less than the difference of threshold value, set C1The X of the insideiTo be more than the difference of threshold value.
C0={ X1,X2,…,XK}Xi≤T (7)
C1={ Xt+1,Xt+2,…,XN}Xi>T (8)
Every XiThe probability of appearance is pi=1/N.
Belong to C0And C1Class occur probability be respectively:
C0Class and C1Class occur average be:
C0Class and C1Class occur variance be:
It is possible thereby to calculate to obtain C0Class and C1The variance within clusters of classInter-class varianceAnd population varianceRespectively:
WhenWhen maximum, the value of corresponding threshold value T is optimal threshold.
The corresponding curve of spectrum of optimal threshold criterion pigment cake spectral reflectivity and mural painting paint spectral reflectivity Whether the corresponding curve of spectrum is a kind of curve of substance.
SAM algorithms, SID algorithms, SCF algorithms and the present invention are compared, and the master sample selected in experimentation is real Test the existing azurite of room pigment, malachite green, red lead, white clam, bright red, cyanine, eosin, gamboge, sky blue, kermes, ochre and Zhu's fat 12 Kind pigment, pigment to be measured are the pigment cake that oneself makes, and the pigment of selection is existing all 12 kinds of pigment in storehouse, matched Mode matches between pigment of the same race;It can be seen that by the comparison of above four kinds of algorithms and Spectral matching carried out to pigment of the same race When, the matching rate sections of SAM algorithms is in 94%-97%, and the matching rate section of SCF algorithms is in 95-98%, the matching of SID algorithms Rate section is in 96%-98%, and matching rate section of the invention is 98%~100%, and the present invention is matched in each group of pigment In comparison, matching rate is above other three kinds of algorithms, it can be seen that the ratio of precision classics spectrum of the matching result of the present invention Matching algorithm is high, and matching result is as shown in table 1:
Table 1

Claims (3)

1. a kind of Spectral matching algorithm based on improved edit-distance, which is characterized in that comprise the following steps:
1) normal pigment block is made;
2) spectral reflectivity of mural painting paint and the spectral reflectivity of normal pigment block are gathered;
3) spectral reflectivity and mark of the mural painting paint obtained using the Spectral matching algorithm of improved edit-distance to step 2) The spectral reflectivity of quasi- pigment cake carries out Spectral matching, completes the Spectral matching based on improved edit-distance.
2. the Spectral matching algorithm of improved edit-distance according to claim 1, which is characterized in that step 1) Plays face Material block manufacturing process be:Block pigment with clear water is impregnated with, then the gelatin of phase homogenous quantities is added in clear water, then with breast Alms bowl is ground, and applies one layer of calcium carbonate on the surface of substrate, grid is depicted after drying, then the fuel after grinding is coated to carbonic acid On calcium, normal pigment block is obtained after drying.
3. the Spectral matching algorithm of improved edit-distance according to claim 1, which is characterized in that in step 2), pass through The spectral reflectivity of spectrophotometer acquisition mural painting paint and the spectral reflectivity of normal pigment block.
CN201711227706.1A 2017-11-29 2017-11-29 A kind of Spectral matching algorithm based on improved edit-distance Pending CN108052962A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201711227706.1A CN108052962A (en) 2017-11-29 2017-11-29 A kind of Spectral matching algorithm based on improved edit-distance

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711227706.1A CN108052962A (en) 2017-11-29 2017-11-29 A kind of Spectral matching algorithm based on improved edit-distance

Publications (1)

Publication Number Publication Date
CN108052962A true CN108052962A (en) 2018-05-18

Family

ID=62121310

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201711227706.1A Pending CN108052962A (en) 2017-11-29 2017-11-29 A kind of Spectral matching algorithm based on improved edit-distance

Country Status (1)

Country Link
CN (1) CN108052962A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020014842A1 (en) * 2018-07-16 2020-01-23 深圳达闼科技控股有限公司 Substance detection method and apparatus, terminal, and readable storage medium
CN114279976A (en) * 2021-12-27 2022-04-05 北京建筑大学 Mural soluble salt content detection method based on reflection spectrum

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102879099A (en) * 2012-08-08 2013-01-16 北京建筑工程学院 Wall painting information extraction method based on hyperspectral imaging
CN103400151A (en) * 2013-08-16 2013-11-20 武汉大学 Optical remote-sensing image, GIS automatic registration and water body extraction integrated method
CN103500343A (en) * 2013-09-30 2014-01-08 河海大学 Hyperspectral image classification method based on MNF (Minimum Noise Fraction) transform in combination with extended attribute filtering
CN104036289A (en) * 2014-06-05 2014-09-10 哈尔滨工程大学 Hyperspectral image classification method based on spatial and spectral features and sparse representation
CN107392925A (en) * 2017-08-01 2017-11-24 西安电子科技大学 Remote sensing image terrain classification method based on super-pixel coding and convolutional neural networks

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102879099A (en) * 2012-08-08 2013-01-16 北京建筑工程学院 Wall painting information extraction method based on hyperspectral imaging
CN103400151A (en) * 2013-08-16 2013-11-20 武汉大学 Optical remote-sensing image, GIS automatic registration and water body extraction integrated method
CN103500343A (en) * 2013-09-30 2014-01-08 河海大学 Hyperspectral image classification method based on MNF (Minimum Noise Fraction) transform in combination with extended attribute filtering
CN104036289A (en) * 2014-06-05 2014-09-10 哈尔滨工程大学 Hyperspectral image classification method based on spatial and spectral features and sparse representation
CN107392925A (en) * 2017-08-01 2017-11-24 西安电子科技大学 Remote sensing image terrain classification method based on super-pixel coding and convolutional neural networks

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
王建黑: "光谱图像技术在柑橘采摘和采后处理中的应用研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020014842A1 (en) * 2018-07-16 2020-01-23 深圳达闼科技控股有限公司 Substance detection method and apparatus, terminal, and readable storage medium
CN114279976A (en) * 2021-12-27 2022-04-05 北京建筑大学 Mural soluble salt content detection method based on reflection spectrum
CN114279976B (en) * 2021-12-27 2023-09-19 北京建筑大学 Method for detecting content of soluble salt in wall painting based on reflection spectrum

Similar Documents

Publication Publication Date Title
CN111242224B (en) Multi-source remote sensing data classification method based on unmanned aerial vehicle extraction classification sample points
Ballabeni et al. Advances in image pre-processing to improve automated 3D reconstruction
CN108052962A (en) A kind of Spectral matching algorithm based on improved edit-distance
AU2012332053B2 (en) Determining colour values in hyperspectral or multispectral images
Pichi-Sermolli An index for establishing the degree of maturity in plant communities
CN108830844A (en) A kind of facilities vegetable extracting method based on multidate high-resolution remote sensing image
Musicco et al. Automatic point cloud segmentation for the detection of alterations on historical buildings through an unsupervised and clustering-based Machine Learning approach
Feng et al. An improved nonlocal sparse unmixing algorithm for hyperspectral imagery
Sun et al. Middle Jurassic Ginkgo leaves from the Daohugou area, Inner Mongolia, China and their implication for palaeo-CO2 reconstruction
Elfiky et al. Color constancy using 3D scene geometry derived from a single image
Axelsson et al. The use of dual-wavelength airborne laser scanning for estimating tree species composition and species-specific stem volumes in a boreal forest
CN106067171B (en) A kind of high spectrum image essence is decomposed and image partition method
CN113160077B (en) High-fidelity digital restoration method for color of faded colored mural
CN106952283B (en) Image partition method and device
Brøns et al. ‘A lost chapter of ancient art’: archaeometric examinations of panel paintings from Roman Egypt
Liu Supervised classification and unsupervised classification
CN111091087B (en) Land coverage extraction algorithm based on multi-view collaborative canonical correlation forest remote sensing image
Lei et al. Study on the Application of Ganan Hakka Architectural Elements in Modern Architectural Interior Design
Zheng et al. The method of color element allocation of ornamental plants considering water condition
Hill et al. Material Choice and Interaction on Brown’s Bo om
Lu et al. Color constancy using stage classification
CN111008653B (en) Clustering optimization method for mixed pigment information unmixing
CN110427950A (en) Purple soil soil image shadow detection method
Sun et al. Performance Evaluation of Color Descriptors under Illumination Variation
Ranjan et al. 3-D Material Style Transfer for Reconstructing Unknown Appearance in Complex Natural Materials

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20180518