CN101996328A - Wood identification method - Google Patents
Wood identification method Download PDFInfo
- Publication number
- CN101996328A CN101996328A CN 201010506395 CN201010506395A CN101996328A CN 101996328 A CN101996328 A CN 101996328A CN 201010506395 CN201010506395 CN 201010506395 CN 201010506395 A CN201010506395 A CN 201010506395A CN 101996328 A CN101996328 A CN 101996328A
- Authority
- CN
- China
- Prior art keywords
- image
- feature
- timber
- cluster
- weight
- 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.)
- Granted
Links
Abstract
The invention relates to a wood identification method. The method comprises the following steps of: (1) generating characteristic quantities, namely partitioning a wood image, extracting local characteristics of each partitioned image, combining the local characteristics of a plurality of partitioned images to form the characteristics of the wood image, clustering and merging the characteristics of the wood image by clustering, extracting clustered information, and on the basis, screening the clustered information to form the characteristic quantities of the wood image; (2) repeating the step (1) for each image in an N original wood images-containing training sample set so as to generate an N characteristic quantities-containing characteristic set; (3) repeating the step (1) for the wood images to be identified so as to generate the characteristic quantities of the wood images to be identified; and (4) calculating the characteristic quantities of the wood images to be identified and the characteristic set by an earth mover's distance (EMD) algorithm to generate N EMDs, and classifying the N EMDs to obtain an identification result. The method has higher identification rate.
Description
Technical field
The present invention relates to a kind of Wood Identification Method, especially a kind of supplementary means that uses a computer, based on the Wood Identification Method of analyzing image texture.
Background technology
The early stage method of timber identification mainly is to rely on artificial experience and knowledge, and in conjunction with some aids, as key, verge-perforated card, and for example the day for announcing is on October 1st, 1986, notification number is the disclosed number timber of the Chinese patent application of a CN86200457U identification card, or the like, according to macrofeature and microscopic feature, by observing, identification timber is progressively identified in comparison and analysis, this method is subjected to assessor's the influence of subjective factor very big, discrimination is low, cause some preciousnesses, price is higher, or timber with specific use, often be taken as generally and handle,, also can occur blindly using because of discerning unclear causing as for generally using timber with material, the phenomenon of misusing, not only to timber production, sale and use wait each link to make troubles, influence the raising of forest industrial enterprise economic interests, also wasted valuable resource, in addition, because it is very long to rely on artificial recognition methods to expend time in, and has also caused waste of manpower resource to a certain extent.
In recent years, the identification of area of computer aided timber becomes a kind of Wood Identification Method of main flow gradually, as Granted publication day be on August 5th, 2009, notification number is the near infrared spectrum recognition methods of the redwood announced of the Chinese patent of 100523793C, adopt redwood and non-redwood wood sample exactly, utilize the diverse location collection several near infrared spectrum of near infrared spectrum equipment on the wood sample surface, gather spectrum for same sample respectively 3～10 positions, pass through the spectrum pre-service again, as smoothly, baseline correction, first order derivative, second derivative, after polynary scatter correction or the pre-service of data dimensionality reduction, by multivariate data analysis methods such as soft independent modeling classification or least square discriminatory analyses, set up the discrimination model of true and false redwood and timber varieties of trees thereof, thereby, utilize the model of setting up can realize why adding the differentiation of redwood and timber varieties of trees.This method has higher analysis efficiency and accuracy, but since its based on sample be the timber material object, for some timber that is difficult for sampling and carrying, aspect practical operation, exist certain difficulty.Based on lumber fibre or stereogram picture, adopt the recognition methods of machine vision means to overcome the problems referred to above, as everyone knows, timber identification is often determined by the zone of the key feature in the timber picture, and in the timber picture contain fracture, resin canal, the noise region of feature such as damage by worms, go mouldy, then the identification to timber has certain inhibiting effect, existing this type of recognition methods does not have effectively to give prominence to the effect in timber key feature zone, can not well eliminate the influence of noise region, thereby reduce discrimination greatly.
Summary of the invention
The objective of the invention is to overcome in the prior art based on the lower problem of the recognition methods discrimination of timber picture, proposed a kind of Wood Identification Method with higher discrimination.
For achieving the above object, the technical solution adopted in the present invention is:
A kind of Wood Identification Method comprises the steps:
(1) characteristic quantity generates: a timber image block is handled, extract the local feature of every block image, and the local feature of a plurality of piecemeals is combined to form the feature of timber image, with clustering processing the feature of timber image is carried out the cluster merger, extract clustering information, on this basis, clustering information is carried out Screening Treatment, form the characteristic quantity of timber image;
(2) to including concentrated every the image repeating step (1) of training sample that N opens the timber original image, generate a feature set that comprises N characteristic quantity;
(3), generate the characteristic quantity of timber image to be identified to timber image repeating step to be identified (1);
(4) characteristic quantity and the feature set with timber image to be identified generates N EMD distance with the EMD algorithm computation, and N EMD apart from classifying processing, drawn recognition result.
Technique scheme can also be further perfect:
As preferably, further comprise a pre-service substep of handling to carry out prior to piecemeal in the step (1): choose the subregion of a w * h at the obvious position of feature of timber image, and gradation conversion is carried out in this zone, form pretreatment image.
As preferably, the obvious position of feature be meant between two annual ring lines of timber, do not contain fracture, resin canal, any one the position in four of damaging by worms, go mouldy at least.
As preferably, piecemeal handle with pretreatment image according to horizontal m piece, vertically the n piece is divided into mn identical block image, makes mn=m * n.
As preferably, use the Gabor wavelet algorithm to extract the feature of every block image, and form the feature of timber image, Gabor handles and comprises the steps: that (1) carry out the Gabor conversion of s yardstick, an o direction one by one respectively to every block image; (2) eigenwert of every block image of extraction; (3) make os=o * s, every block image is obtained the proper vector that os eigenwert is arranged in 1 * os dimension; (4) every pretreatment image is obtained the eigenmatrix that the capable proper vector of mn is arranged in a mn * os, be called the Gabor feature, called after G.
As preferably, the value of s, o is respectively 4,6, or 5,8.
As preferably, clustering processing comprises the steps: (1) with each row of proper vector G point as the os dimension space, and every pretreatment image forms mn point; (2) with clustering algorithm mn point gathered into k cluster, a point on the corresponding os dimension space of each cluster, i.e. k os dimensional vector; (3) make cluster cluster=[cluster
1, cluster
2..., cluster
k]; (4) write down each cluster and comprise a little number n um=[num
1, num
2..., num
k]; (5) claim cluster, num is a clustering information.
As preferably, Screening Treatment comprises the steps: that (1) makes weight
i=num
i/ mn; (2) weight is once arranged from big to small, i weight makes their ∑ that satisfies condition just before filtering out
1 iWeight
i〉=M (i≤k); (3) make feature=[feature
1, feature
2..., feature
i], feature wherein
i=cluster
i(4) make weight=[weight
1, weight
2..., weight
i], claim weight
iBe feature
iWeight; (5) make signature=[s
1, s
2..., s
i], s wherein
i=(feature
i, weight
i), signature is the characteristic quantity of timber image.
As preferably, the M value is 90%.
As preferably, classify to handle and adopt the SVM algorithm.
Because the employing of technique scheme, the present invention compared with prior art has the following advantages:
The present invention is by the reasonable piecemeal refinement to the timber picture, each characteristic area (is comprised the critical area that has key feature, common texture region, the region of fracture, the resin canal zone, the zone of damaging by worms, the zone etc. of going mouldy) independent, adopt key feature zone and common texture characteristic area content as block image, and to every piecemeal pattern Gabor small echo extraction feature, this way has been strengthened the effect in key feature zone, solved because of whole image being carried out the Gabor small echo and handled the problem that lowers the effect of key feature zone, and, can contain fracture by removal, resin canal, damage by worms, the noise region of going mouldy improves discrimination.
In addition, the present invention has introduced clustering algorithm, and the feature that the Gabor small echo is extracted concentrates, and has reduced the operand that the piecemeal refinement brings for follow-up identifying.
Moreover, because the picture in zone such as contain fracture, resin canal, damage by worms, go mouldy has partly been given up in picture pre-treatment step process, so these zones shared proportion in whole Zhang Mucai wood chip is very little, again the feature after the clustering processing by being set, suitable threshold is screened, can remove these zones effectively, further improve discrimination.
In addition, another advantage of Screening Treatment is when having kept important crucial recognition feature, to have kept original discrimination, and by removing non-key character, improved the execution efficient of identifying.
At last, with existing find the solution the method for characteristic similarity between image different be, classic method is earlier characteristics of image to be discharged in certain sequence, then a plurality of features of different images are carried out the man-to-man similarity (or distance) of simply asking one by one, at last these similarities (or distance) are added up summation as the final foundation of judging the different images similarity degree, its shortcoming is the influence that the similarity of different images feature is put in order by characteristics of image, can not make rational judgement to the relative degree of characteristics of image.And the present invention adopts EMD(Earth Mover ' s Distance, this measure derives from famous cargo transfer problem, it is a kind of characteristic similarity that can calculate different picture effectively and comprised, and carry out the part Matching Algorithm) come the similarity of computed image feature, can solve the aforementioned problems in the prior effectively, eliminate characteristics of image and put in order and calculate the adverse effect of bringing to similarity.
Description of drawings
Fig. 1 is a kind of process flow diagram of the present invention.
Embodiment
Below in conjunction with accompanying drawing, the present invention will be further described:
Wood Identification Method as shown in Figure 1 mainly comprises the steps:
(1) characteristic quantity generates: a timber image block is handled, extract the local feature of every block image, and the local feature of a plurality of piecemeals is combined to form the feature of timber image, with clustering processing the feature of timber image is carried out the cluster merger, extract clustering information, on this basis, clustering information is carried out Screening Treatment, form the characteristic quantity of timber image;
(2) to including concentrated every the image repeating step (1) of training sample that N opens the timber original image, generate a feature set that comprises N characteristic quantity;
(3), generate the characteristic quantity of timber image to be identified to timber image repeating step to be identified (1);
(4) characteristic quantity of timber image to be identified and feature set are generated N EMD distance with the EMD algorithm computation, N EMD apart from the processing of classifying of employing SVM algorithm, drawn recognition result.
EMD(earth mover ' s distance) is based on a kind of of famous cargo transfer problem (Hitchcock, 1941) and can be used for effectively calculating the characteristic similarity that different picture comprised, and can carry out a kind of algorithm of part coupling.
Its formula is as follows:
Wherein P, Q represent two signature, d
IjBe the ground distance between feature i, the j, f
IjBe the flow of i to j.
In addition, in step (1), also need to carry out a pre-service substep of handling to carry out prior to piecemeal: choose the subregion of a w * h at the obvious position of feature of timber image, and gradation conversion is carried out in this zone, form pretreatment image.The obvious position of feature be meant between two annual ring lines of timber, do not contain fracture, resin canal, any one the position in four of damaging by worms, go mouldy at least, in the present embodiment, w, h value are 100, pretreatment image is 256 grades of gray level images.
Above-mentioned piecemeal is handled pretreatment image is divided into mn identical block image according to horizontal m piece, vertical n piece, makes mn=m * n, and in the present embodiment, the span of m, n is 4～7.
Use the Gabor wavelet algorithm to extract the feature of every block image, and form the feature of timber image, Gabor handles and comprises the steps:
(1) respectively every block image is carried out the Gabor conversion of s yardstick, an o direction one by one;
(2) eigenwert of every block image of extraction;
(3) make os=o * s, every block image is obtained the proper vector that os eigenwert is arranged in 1 * os dimension; In the present embodiment, the value of s, o is respectively 4,6, or 5,8;
(4) every pretreatment image is obtained the eigenmatrix that the capable proper vector of mn is arranged in a mn * os, be called the Gabor feature, called after G.
The Gabor small echo is the unique Gabor function that can obtain spatial domain and frequency-domain combined uncertainty relation lower limit as the strong instrument of multiple dimensioned expression of image and analysis, through being often used as wavelet basis function, image is carried out various analyses.
The kernel function of two-dimensional Gabor Wavelets of the present invention is as follows:
?
Wherein, u, v are respectively direction and scale factor, are row vector, and x, y are two-dimensional coordinate.
Be the centre frequency of wave filter,
Embodied the directional selectivity of wave filter.
Clustering processing comprises the steps:
(1) with each capable point as the os dimension space of proper vector G, every pretreatment image forms mn point;
(2) with clustering algorithm mn point gathered into k cluster, a point on the corresponding os dimension space of each cluster, i.e. k os dimensional vector;
(3) make cluster cluster=[cluster
1, cluster
2..., cluster
k];
(4) write down each cluster and comprise a little number n um=[num
1, num
2..., num
k]
(5) claim cluster, num is a clustering information.
As preferably, Screening Treatment comprises the steps:
(1) makes weight
i=num
i/ mn;
(2) weight is once arranged from big to small, i weight makes their ∑ that satisfies condition just before filtering out
1 iWeight
i(i≤k), in the present embodiment, the M value is 90% to 〉=M;
(3) make feature=[feature
1, feature
2..., feature
i], feature wherein
i=cluster
i
(4) make weight=[weight
1, weight
2..., weight
i], claim weight
iBe feature
iWeight;
(5) make signature=[s
1, s
2..., s
i], s wherein
i=(feature
i, weight
i), signature is the characteristic quantity of timber image.
Should be understood that this embodiment only to be used to the present invention is described and be not used in and limit the scope of the invention.Should be understood that in addition those skilled in the art can make various changes or modifications the present invention after the content of having read the present invention's instruction, these equivalent form of values fall within the application's appended claims institute restricted portion equally.
Claims (10)
1. a Wood Identification Method is characterized in that, comprises the steps:
(1) characteristic quantity generates: a timber image block is handled, extract the local feature of every block image, and the local feature of a plurality of piecemeals is combined to form the feature of described timber image, with clustering processing the feature of described timber image is carried out the cluster merger, extract clustering information, on this basis, clustering information is carried out Screening Treatment, form the characteristic quantity of described timber image;
(2) to including concentrated every the image repeating step (1) of training sample that N opens the timber original image, generate a feature set that comprises N characteristic quantity;
(3), generate the characteristic quantity of timber image to be identified to timber image repeating step to be identified (1);
(4) characteristic quantity and the described feature set with timber image to be identified generates N EMD distance with the EMD algorithm computation, and N EMD apart from classifying processing, drawn recognition result.
2. Wood Identification Method according to claim 1, it is characterized in that, further comprise a pre-service substep of handling to carry out prior to piecemeal in the step (1): the subregion of choosing a w * h at the obvious position of feature of described timber image, and this zone carried out gradation conversion, form pretreatment image.
3. Wood Identification Method according to claim 2, it is characterized in that: the obvious position of described feature be meant between two annual ring lines of timber, do not contain fracture, resin canal, any one the position in four of damaging by worms, go mouldy at least, described w, h value are 100, and described pretreatment image is 256 grades of gray level images.
4. according to claim 1 or 2 or 3 described Wood Identification Method, it is characterized in that: described piecemeal is handled described pretreatment image is divided into mn identical block image according to horizontal m piece, vertical n piece, make mn=m * n, the span of m, n is 4～7.
5. Wood Identification Method according to claim 4 is characterized in that, uses the Gabor wavelet algorithm to extract the feature of every described block image, and forms the feature of described timber image, and described Gabor handles and comprises the steps:
(1) respectively every described block image is carried out the Gabor conversion of s yardstick, an o direction one by one;
(2) eigenwert of every block image of extraction;
(3) make os=o * s, every described block image is obtained the proper vector that os eigenwert is arranged in 1 * os dimension;
(4) every described pretreatment image is obtained the eigenmatrix that the capable described proper vector of mn is arranged in a mn * os, be called the Gabor feature, called after G.
6. Wood Identification Method according to claim 5 is characterized in that the value of s, o is respectively 4,6 or 5,8.
7. Wood Identification Method according to claim 6 is characterized in that described clustering processing comprises the steps:
(1) with each capable point as the os dimension space of proper vector G, every described pretreatment image forms mn point;
(2) with clustering algorithm mn point gathered into k cluster, a point on the corresponding os dimension space of each cluster, i.e. k os dimensional vector;
(3) make cluster cluster=[cluster
1, cluster
2..., cluster
k];
(4) write down each cluster and comprise a little number n um=[num
1, num
2..., num
k];
(5) claim cluster, num is described clustering information.
8. Wood Identification Method according to claim 7 is characterized in that described Screening Treatment comprises the steps:
(1) makes weight
i=num
i/ mn;
(2) weight is once arranged from big to small, i weight makes their ∑ that satisfies condition just before filtering out
1 iWeight
i〉=M (i≤k);
(3) make feature=[feature
1, feature
2..., feature
i], feature wherein
i=cluster
i
(4) make weight=[weight
1, weight
2..., weight
i], claim weight
iBe feature
iWeight;
(5) make signature=[s
1, s
2..., s
i], s wherein
i=(feature
i, weight
i), signature is the characteristic quantity of described timber image.
9. Wood Identification Method according to claim 8 is characterized in that, the M value is 90%.
10. according to claim 1 or 2 or 3 described Wood Identification Method, it is characterized in that: described classification is handled and is adopted the SVM algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201010506395A CN101996328B (en) | 2010-10-14 | 2010-10-14 | Wood identification method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201010506395A CN101996328B (en) | 2010-10-14 | 2010-10-14 | Wood identification method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101996328A true CN101996328A (en) | 2011-03-30 |
| CN101996328B CN101996328B (en) | 2012-10-10 |
Family
ID=43786466
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201010506395A Expired - Fee Related CN101996328B (en) | 2010-10-14 | 2010-10-14 | Wood identification method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101996328B (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102214305A (en) * | 2011-04-08 | 2011-10-12 | 大连理工大学 | Method for taking evidence for source of printing paper sheet by using grain characteristic |
| CN104794486A (en) * | 2015-04-10 | 2015-07-22 | 电子科技大学 | Video smoke detecting method based on multi-feature fusion |
| CN105354823A (en) * | 2015-09-28 | 2016-02-24 | 佛山市朗达信息科技有限公司 | Tree-ring image edge extraction and segmentation system |
| CN108229314A (en) * | 2017-11-28 | 2018-06-29 | 深圳市商汤科技有限公司 | Searching method, device and the electronic equipment of target person |
| CN108256550A (en) * | 2017-12-14 | 2018-07-06 | 北京木业邦科技有限公司 | A kind of timber classification update method and device |
| CN108262809A (en) * | 2017-12-14 | 2018-07-10 | 北京木业邦科技有限公司 | Plank processing method, device, electronic equipment and medium based on artificial intelligence |
| CN108663360A (en) * | 2018-04-23 | 2018-10-16 | 安徽农业大学 | The chemical identification method of the unapparent tree age of growth ring |
| CN112723076A (en) * | 2021-01-07 | 2021-04-30 | 昆明理工大学 | Fault diagnosis method for guide shoe of elevator |
| CN112767387A (en) * | 2021-01-29 | 2021-05-07 | 中华人民共和国张家港海关 | Automatic wood image identification method based on block gradient weighting |
| CN114397250A (en) * | 2021-12-27 | 2022-04-26 | 中国林业科学研究院木材工业研究所 | Wood identification method based on spectral features and image features |
| CN112767387B (en) * | 2021-01-29 | 2024-04-30 | 中华人民共和国张家港海关 | Automatic wood image recognition method based on block gradient weighting |
-
2010
- 2010-10-14 CN CN201010506395A patent/CN101996328B/en not_active Expired - Fee Related
Non-Patent Citations (5)
| Title |
|---|
| 《大众科技》 20100831 徐一清 木材纹理识别算法研究进展 145-146 1-10 , 第8期 * |
| 《林业机械与木工设备》 20091031 何涛等 基于两方向二维主成分分析木材识别的研究 12-14 1-10 第37卷, 第10期 * |
| 《浙江林学院学报》 20091231 汪杭军等 木材识别方法研究综述 896-902 1-10 第26卷, 第6期 * |
| 《计算机工程与应用》 20090831 张召等 基于纹理提取和SVM技术的自动木材缺陷识别 219-223 1-10 第45卷, 第23期 * |
| 《郑州大学学报(理学版)》 20100331 吴思远等 基于2D Gabor小波和HSV空间的木材缺陷检测 93-97 1-10 第42卷, 第1期 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102214305A (en) * | 2011-04-08 | 2011-10-12 | 大连理工大学 | Method for taking evidence for source of printing paper sheet by using grain characteristic |
| CN104794486A (en) * | 2015-04-10 | 2015-07-22 | 电子科技大学 | Video smoke detecting method based on multi-feature fusion |
| CN104794486B (en) * | 2015-04-10 | 2018-10-16 | 电子科技大学 | Video smoke detection method based on multi-feature fusion |
| CN105354823A (en) * | 2015-09-28 | 2016-02-24 | 佛山市朗达信息科技有限公司 | Tree-ring image edge extraction and segmentation system |
| CN110431560A (en) * | 2017-11-28 | 2019-11-08 | 深圳市商汤科技有限公司 | The searching method and device of target person, equipment, program product and medium |
| CN108229314A (en) * | 2017-11-28 | 2018-06-29 | 深圳市商汤科技有限公司 | Searching method, device and the electronic equipment of target person |
| CN110431560B (en) * | 2017-11-28 | 2023-12-19 | 深圳市商汤科技有限公司 | Target person searching method, device, equipment and medium |
| US10891465B2 (en) | 2017-11-28 | 2021-01-12 | Shenzhen Sensetime Technology Co., Ltd. | Methods and apparatuses for searching for target person, devices, and media |
| CN108256550A (en) * | 2017-12-14 | 2018-07-06 | 北京木业邦科技有限公司 | A kind of timber classification update method and device |
| CN108262809A (en) * | 2017-12-14 | 2018-07-10 | 北京木业邦科技有限公司 | Plank processing method, device, electronic equipment and medium based on artificial intelligence |
| CN108663360A (en) * | 2018-04-23 | 2018-10-16 | 安徽农业大学 | The chemical identification method of the unapparent tree age of growth ring |
| CN112723076A (en) * | 2021-01-07 | 2021-04-30 | 昆明理工大学 | Fault diagnosis method for guide shoe of elevator |
| CN112767387A (en) * | 2021-01-29 | 2021-05-07 | 中华人民共和国张家港海关 | Automatic wood image identification method based on block gradient weighting |
| CN112767387B (en) * | 2021-01-29 | 2024-04-30 | 中华人民共和国张家港海关 | Automatic wood image recognition method based on block gradient weighting |
| CN114397250A (en) * | 2021-12-27 | 2022-04-26 | 中国林业科学研究院木材工业研究所 | Wood identification method based on spectral features and image features |
| CN114397250B (en) * | 2021-12-27 | 2024-02-13 | 中国林业科学研究院木材工业研究所 | Wood identification method based on spectral features and image features |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101996328B (en) | 2012-10-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101996328B (en) | Wood identification method | |
| Williams et al. | Advanced image classification using wavelets and convolutional neural networks | |
| Parisi-Baradad et al. | Otolith shape contour analysis using affine transformation invariant wavelet transforms and curvature scale space representation | |
| Cope et al. | Plant texture classification using gabor co-occurrences | |
| CN110837768B (en) | Online detection and identification method for rare animal protection | |
| CN102254188B (en) | Palmprint recognizing method and device | |
| CN105574063A (en) | Image retrieval method based on visual saliency | |
| CN104217221A (en) | Method for detecting calligraphy and paintings based on textural features | |
| CN101526994B (en) | Fingerprint image segmentation method irrelevant to collecting device | |
| Badawi et al. | A hybrid memetic algorithm (genetic algorithm and great deluge local search) with back-propagation classifier for fish recognition | |
| CN103390170A (en) | Surface feature type texture classification method based on multispectral remote sensing image texture elements | |
| CN102136074B (en) | Man-machine interface (MMI) based wood image texture analyzing and identifying method | |
| CN104268587B (en) | False fingerprint detection method based on finger wave conversion and SVM | |
| Wongthanavasu | Cellular automata for medical image processing | |
| Farouk et al. | Microarray spot segmentation algorithm based on integro-differential operator | |
| CN106778897B (en) | Plant species recognition methods twice based on COS distance and center profile distance | |
| Panigrahi et al. | Evaluations of fractal geometry and invariant moments for shape classification of corn germplasm | |
| CN111127407B (en) | Fourier transform-based style migration forged image detection device and method | |
| Jasani et al. | Review of shape and texture feature extraction techniques for fruits | |
| Lin et al. | Edge detection in the feature space | |
| CN108960246A (en) | A kind of binary conversion treatment device and method for image recognition | |
| Rajesh et al. | Intelligent Detection of Disease in Plant Leaf using SVM | |
| CN110136112B (en) | Computer-aided detection algorithm based on mammary gland X-ray photography calcification | |
| Bailey et al. | Image analysis of weaverbird nests reveals signature weave textures | |
| Kaur et al. | Intensity based image segmentation using wavelet analysis and clustering techniques |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20121010 Termination date: 20141014 |
|
| EXPY | Termination of patent right or utility model |