CN1806501A - Marine phytoplankton automatic distinguishing method and apparatus - Google Patents
Marine phytoplankton automatic distinguishing method and apparatus Download PDFInfo
- Publication number
- CN1806501A CN1806501A CN 200510042345 CN200510042345A CN1806501A CN 1806501 A CN1806501 A CN 1806501A CN 200510042345 CN200510042345 CN 200510042345 CN 200510042345 A CN200510042345 A CN 200510042345A CN 1806501 A CN1806501 A CN 1806501A
- Authority
- CN
- China
- Prior art keywords
- marine phytoplankton
- graphic feature
- phytoplankton
- marine
- database
- 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
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
An automatic identification method for ocean phytoplankton, comprising following steps: (1) making grape feature of known ocean phytoplankton be corresponding to its description and forming a data base of phytoplankton to store in computer memory in advance; (2) collecting grape feature of identifying phytoplankton with digital microscope and sending into computer; (3) comparing collected grape feature with that of in data base, and outputting description of known phytoplankton when the osculation reach a regulated value. The invention combines digital microscope with computer system to identify ocean phytoplankton automatically, fast and accurately.
Description
Technical field
The present invention relates to recognition methods and the device of marine phytoplankton, specifically be meant the method and the device that utilize digital microscope and computer system that marine phytoplankton is discerned automatically.
Background technology
The phytoplankton classification identifies it is the important content of marine organisms research, the domestic and international at present method that all adopts light microscope manually to identify, promptly artificial geometry, the superficial makings that uses the microscopic examination marine phytoplankton, person's experience or search related data and compare according to the observation again is to judge the classification of phytoplankton to be identified.Obviously, this kind method is very consuming time, and operating efficiency is extremely low, and its accuracy depends on observer's knowledge and experience itself, and general personnel can't carry out.
Marine phytoplankton research has great importance for rationally utilizing marine resources, protection and improving marine environment.The marine phytoplankton classification identifies it is primary, the critical step of the research, basic effect is played in other research, therefore, seek a kind of accurately, high efficiency automatic identifying method, develop a kind of accuracy height, automatic identification equipment that recognition efficiency is high, will have great importance.
Summary of the invention
The invention provides a kind of marine phytoplankton automatic distinguishing method and device, its main purpose is to overcome original employing light microscope and carries out that artificial authentication method not only expends the plenty of time, efficient is extremely low, and its accuracy too relies on observer's knowledge and experience and defective that can not popularization and application.
The present invention adopts following technical scheme: marine phytoplankton automatic distinguishing method, may further comprise the steps: 1) that graphic feature and the description thereof of known marine phytoplankton is corresponding one by one, form a marine phytoplankton database, be stored in advance in the memory of calculator; 2) gather the graphic feature of marine phytoplankton to be identified with digital microscope, and be sent to calculator; 3) utilize calculator that the known marine phytoplankton graphic feature in the marine phytoplankton database in the marine phytoplankton graphic feature to be identified of digital microscope collection and the memory is contrasted one by one, when the identical rate of marine phytoplankton graphic feature to be identified and known marine phytoplankton graphic feature reaches a setting, the description of exporting this known marine phytoplankton.
In the preceding method, the graphic feature of marine phytoplankton comprises its length, width, area, aspect ratio, ovality, symmetry, superficial makings.
Preceding method further comprises: if there is not to reach with the identical rate of marine phytoplankton graphic feature to be identified the graphic feature of described setting in the marine phytoplankton database, graphic feature that then will this marine phytoplankton to be identified is increased in the marine phytoplankton database, and being this marine phytoplankton definition description, correspondence deposits the marine phytoplankton database in.
The marine phytoplankton automatic identification equipment comprises: a digital microscope as image collecting device is connected to a computer system; One computer system comprises: a graphic feature Database Unit is used to store graphic feature and the description thereof of known marine phytoplankton; One graphic feature extraction unit is used for the graphic feature by digital microscope extraction marine phytoplankton to be identified; One image identification unit, the graphic feature that is used for the marine phytoplankton that graphic feature that the graphic feature extraction unit is extracted and graphic feature Database Unit store contrasts one by one, when identical rate reaches a preset value, will be sent to output unit with the corresponding description of this marine phytoplankton; One output unit is used for result's output that image identification unit is sent.
In the aforementioned means, output unit is a PRN device or a display device.
By the above-mentioned description of this invention as can be known; the present invention creatively combines digital microscope and computer system; to be applied to the identification of marine phytoplankton; can be automatic; fast; identify marine phytoplankton exactly; improve recognition efficiency greatly; the artificial degree of participation of this identifying is lower; do not rely on operator's professional experiences; thereby can improve the marine monitoring ability of China and the technical merit of marine eco-environment fast monitored greatly; to protecting and improving marine environment and will play important effect, can be widely used in the marine ecology resource investigation; red tide research; environmental monitoring; aquaculture; the water ballast monitoring; water quality monitoring; geology; oil exploration; little algae cultivation and drug development and legal medical expert such as identify at the field.
Description of drawings
Fig. 1 is system architecture of the present invention and data flow schematic diagram;
Fig. 2 is a flow chart of the present invention.
Specific embodiment
Below in conjunction with Fig. 1 and Fig. 2, describe a specific embodiment of the present invention in detail.
With reference to Fig. 1, be the system construction drawing of marine phytoplankton automatic identification equipment of the present invention, this device comprises a digital microscope 1, one computer system 2 and an output unit 3.
Computer system 2 adopts existing common PC to get final product, its CPU, internal memory, hard disk size and interface configuration can be configured as required, and its operating system adopts Microsoft Windows 2000Server Simplified ChineseVersion with Service Pack 4.Output unit 3 is a display or printer, is connected to computer system 2 by standard display interface or standard print interface, can be other output equipment also, is used for showing or printing recognition result.
Graphic feature extraction unit 21, graphic feature Database Unit 22, image identification unit 23 are for being installed on the software on the computer system 2, and the developing instrument that this software adopted is as follows:
The development environment of system adopts Microsoft Visual Studio.NET 2003, develops based on Microsoft WebService technology;
System design aids adopts Microsoft Visio 2003 for Visual Studio;
The system documentation instrument adopts Microsoft Word 2000, Microsoft Excel 2000;
Operating system adopts Microsoft Windows 2000 Server Simplified Chinese Version withService Pack 4, and Microsoft Internet Explore 6.0 or above version are installed, the above version of Microsoft XML3.0 is used for the support of XML;
The source code control tool adopts Microsoft SourceSafe 6;
Microsoft Project 2000 is adopted in project process control;
Testing tool adopts Microsoft Test Center 1.2;
Background data base adopts Mi crosoft SQL server 2000.
Above-mentioned developing instrument is adopted by present embodiment, but is not limited thereto, and in like manner can adopt other developing instrument to develop, even software solidification can be realized in hardware chip.Below in conjunction with Fig. 1, Fig. 2 the course of work of this system is described.
The graphic feature that has known marine phytoplankton in the graphic feature Database Unit 22 in advance, and, the graphic feature of each marine phytoplankton is described corresponding to one, and this description has comprised the relevant information of this kind marine phytoplankton, as title, characteristics, affiliated classification or the like.
As seen, this system can not only discern marine phytoplankton automatically, also provides inlet for newfound marine phytoplankton joins in the graphic feature Database Unit 22, thereby can make constantly self-perfection of system itself.
Above-mentioned only is a specific embodiment of the present invention, but design concept of the present invention is not limited thereto, and allly utilizes this design that the present invention is carried out the change of unsubstantiality, all should belong to the behavior of invading protection domain of the present invention.
Claims (5)
1, marine phytoplankton automatic distinguishing method may further comprise the steps:
1) graphic feature and the description thereof of known marine phytoplankton is corresponding one by one, form a marine phytoplankton database, be stored in advance in the memory of calculator;
2) gather the graphic feature of marine phytoplankton to be identified with digital microscope, and be sent to calculator;
3) utilize calculator that the known marine phytoplankton graphic feature in the marine phytoplankton database in the marine phytoplankton graphic feature to be identified of digital microscope collection and the memory is contrasted one by one, when the identical rate of marine phytoplankton graphic feature to be identified and known marine phytoplankton graphic feature reaches a setting, the description of exporting this known marine phytoplankton.
2, marine phytoplankton automatic distinguishing method as claimed in claim 1, wherein, the graphic feature of marine phytoplankton comprises its length, width, area, aspect ratio, ovality, symmetry, superficial makings.
3, marine phytoplankton automatic distinguishing method as claimed in claim 1, further comprise: if there is not to reach the graphic feature of described setting in the marine phytoplankton database with the identical rate of marine phytoplankton graphic feature to be identified, graphic feature that then will this marine phytoplankton to be identified is increased in the marine phytoplankton database, and being this marine phytoplankton definition description, correspondence deposits the marine phytoplankton database in.
4, marine phytoplankton automatic identification equipment comprises:
One digital microscope as image collecting device is connected to a computer system;
One computer system comprises: a graphic feature Database Unit is used to store graphic feature and the description thereof of known marine phytoplankton; One graphic feature extraction unit is used for the graphic feature by digital microscope extraction marine phytoplankton to be identified; One image identification unit, the graphic feature that is used for the marine phytoplankton that graphic feature that the graphic feature extraction unit is extracted and graphic feature Database Unit store contrasts one by one, when identical rate reaches a preset value, will be sent to output unit with the corresponding description of this marine phytoplankton;
One output unit is used for result's output that image identification unit is sent.
5, marine phytoplankton automatic identification equipment as claimed in claim 3, wherein, described output unit is a PRN device or a display device.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN 200510042345 CN1806501B (en) | 2005-01-17 | 2005-01-17 | Marine phytoplankton automatic distinguishing method and apparatus |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN 200510042345 CN1806501B (en) | 2005-01-17 | 2005-01-17 | Marine phytoplankton automatic distinguishing method and apparatus |
Publications (2)
Publication Number | Publication Date |
---|---|
CN1806501A true CN1806501A (en) | 2006-07-26 |
CN1806501B CN1806501B (en) | 2011-08-24 |
Family
ID=36838729
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN 200510042345 Expired - Fee Related CN1806501B (en) | 2005-01-17 | 2005-01-17 | Marine phytoplankton automatic distinguishing method and apparatus |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN1806501B (en) |
Cited By (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101165708B (en) * | 2006-10-19 | 2010-05-26 | 华硕电脑股份有限公司 | Image identification method and system |
CN101975849A (en) * | 2010-09-25 | 2011-02-16 | 宁波大学 | Quick qualitatively and quantitatively optimizing method of phytoplankton |
CN102169582A (en) * | 2011-04-22 | 2011-08-31 | 中科怡海高新技术发展江苏股份公司 | Pattern-identification-based blue-green alga identification method |
CN101403741B (en) * | 2008-10-20 | 2012-07-18 | 中国科学院合肥物质科学研究院 | Plant leaf digital information collection and automatic recognition system and method based on multiple optical spectrum |
CN103345617A (en) * | 2013-06-19 | 2013-10-09 | 成都中医药大学 | Method and system for recognizing traditional Chinese medicine |
JP2016095259A (en) * | 2014-11-17 | 2016-05-26 | 横河電機株式会社 | Plankton measurement system and plankton measurement method |
CN106525855A (en) * | 2016-12-07 | 2017-03-22 | 无锡艾科瑞思产品设计与研究有限公司 | Residual quantity detection system for hospital plate washer |
CN107153844A (en) * | 2017-05-12 | 2017-09-12 | 上海斐讯数据通信技术有限公司 | The accessory system being improved to flowers identifying system and the method being improved |
CN107330440A (en) * | 2017-05-17 | 2017-11-07 | 天津大学 | Sea state computational methods based on image recognition |
CN109165596A (en) * | 2018-08-24 | 2019-01-08 | 福建铁工机智能机器人有限公司 | A kind of agricultural product source tracing method based on wisdom rural area AI system |
CN109490301A (en) * | 2018-10-24 | 2019-03-19 | 深圳市锦润防务科技有限公司 | It is a kind of for monitor on floating platform adhere to analyte detection method, system and storage medium |
CN110458107A (en) * | 2019-08-13 | 2019-11-15 | 北京百度网讯科技有限公司 | Method and apparatus for image recognition |
-
2005
- 2005-01-17 CN CN 200510042345 patent/CN1806501B/en not_active Expired - Fee Related
Cited By (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101165708B (en) * | 2006-10-19 | 2010-05-26 | 华硕电脑股份有限公司 | Image identification method and system |
CN101403741B (en) * | 2008-10-20 | 2012-07-18 | 中国科学院合肥物质科学研究院 | Plant leaf digital information collection and automatic recognition system and method based on multiple optical spectrum |
CN101975849A (en) * | 2010-09-25 | 2011-02-16 | 宁波大学 | Quick qualitatively and quantitatively optimizing method of phytoplankton |
CN101975849B (en) * | 2010-09-25 | 2013-07-17 | 宁波大学 | Quick qualitatively and quantitatively optimizing method of phytoplankton |
CN102169582A (en) * | 2011-04-22 | 2011-08-31 | 中科怡海高新技术发展江苏股份公司 | Pattern-identification-based blue-green alga identification method |
CN102169582B (en) * | 2011-04-22 | 2013-06-12 | 中科怡海高新技术发展江苏股份公司 | Pattern-identification-based blue-green alga identification method |
CN103345617A (en) * | 2013-06-19 | 2013-10-09 | 成都中医药大学 | Method and system for recognizing traditional Chinese medicine |
CN103345617B (en) * | 2013-06-19 | 2016-09-07 | 成都中医药大学 | Chinese medicine knows method for distinguishing and system thereof |
JP2016095259A (en) * | 2014-11-17 | 2016-05-26 | 横河電機株式会社 | Plankton measurement system and plankton measurement method |
CN106525855A (en) * | 2016-12-07 | 2017-03-22 | 无锡艾科瑞思产品设计与研究有限公司 | Residual quantity detection system for hospital plate washer |
CN107153844A (en) * | 2017-05-12 | 2017-09-12 | 上海斐讯数据通信技术有限公司 | The accessory system being improved to flowers identifying system and the method being improved |
CN107330440A (en) * | 2017-05-17 | 2017-11-07 | 天津大学 | Sea state computational methods based on image recognition |
CN109165596A (en) * | 2018-08-24 | 2019-01-08 | 福建铁工机智能机器人有限公司 | A kind of agricultural product source tracing method based on wisdom rural area AI system |
CN109490301A (en) * | 2018-10-24 | 2019-03-19 | 深圳市锦润防务科技有限公司 | It is a kind of for monitor on floating platform adhere to analyte detection method, system and storage medium |
CN110458107A (en) * | 2019-08-13 | 2019-11-15 | 北京百度网讯科技有限公司 | Method and apparatus for image recognition |
Also Published As
Publication number | Publication date |
---|---|
CN1806501B (en) | 2011-08-24 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN1806501B (en) | Marine phytoplankton automatic distinguishing method and apparatus | |
Holt et al. | Progress towards an automated trainable pollen location and classifier system for use in the palynology laboratory | |
CN110245657B (en) | Pathological image similarity detection method and detection device | |
Weaver et al. | LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens | |
Mann et al. | Automatic flower detection and phenology monitoring using time‐lapse cameras and deep learning | |
CN112070135A (en) | Power equipment image detection method and device, power equipment and storage medium | |
CN102915432A (en) | Method and device for extracting vehicle-bone microcomputer image video data | |
CN116416884B (en) | Testing device and testing method for display module | |
CN109979546A (en) | Network model analysis platform and construction method based on artificial intelligence number pathology | |
CN111695014A (en) | Method, system, device and storage medium for automatically generating manuscripts based on AI (artificial intelligence) | |
CN113660484A (en) | Audio and video attribute comparison method, system, terminal and medium based on audio and video content | |
CN202815869U (en) | Vehicle microcomputer image and video data extraction apparatus | |
CN114694130A (en) | Method and device for detecting telegraph poles and pole numbers along railway based on deep learning | |
Thammasanya et al. | A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light | |
CN110689447A (en) | Real-time detection method for social software user published content based on deep learning | |
Grandremy et al. | The ZooScan and the ZooCAM zooplankton imaging systems are intercomparable: A benchmark on the Bay of Biscay zooplankton | |
CN115292538A (en) | Map line element extraction method based on deep learning | |
CN117095216B (en) | Model training method, system, equipment and medium based on countermeasure generation network | |
CN102209236B (en) | Information processing system in exam monitoring system and implementation method thereof | |
CN113820718A (en) | Yangtze river aquatic organism investigation management information system based on GIS | |
Tian et al. | A fine-grained dataset for sewage outfalls objective detection in natural environments | |
CN101030163A (en) | Method for deciding human-machine interface testing covering rate | |
Coughlin et al. | Gravity Spy machine learning classifications of LIGO glitches from observing runs O1, O2, O3a, and O3b | |
CN112489013B (en) | Medical image refinement processing system | |
CN113962933A (en) | PCB defect image detection method based on improved YOLOv3 |
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: 20110824 Termination date: 20130117 |
|
CF01 | Termination of patent right due to non-payment of annual fee |