CN110516505A - Parasite egg pictures recognition methods - Google Patents
Parasite egg pictures recognition methods Download PDFInfo
- Publication number
- CN110516505A CN110516505A CN201810487123.0A CN201810487123A CN110516505A CN 110516505 A CN110516505 A CN 110516505A CN 201810487123 A CN201810487123 A CN 201810487123A CN 110516505 A CN110516505 A CN 110516505A
- Authority
- CN
- China
- Prior art keywords
- recognized
- images
- parasite egg
- carries out
- edge detection
- 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.)
- Withdrawn
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20064—Wavelet transform [DWT]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses parasite egg pictures recognition methods, it is related to parasite egg pictures identification, including parasite egg classification sample, images to be recognized, it is further comprising the steps of: S1, obtain the images to be recognized from parasite egg collection point, decomposition field transformation is carried out in the spatial domain, in the multiple scale spaces for decomposing image information；S2 carries out the edge detection of images to be recognized using spatial domain gradient operator；S3 carries out the edge detection of images to be recognized using small echo in image transform domain；S4 extracts the feature vector for the images to be recognized that edge detection obtains in spatial domain and transform domain；S5 carries out pattern-recognition using artificial neural network.The present invention can effectively extract the edge of parasite egg, as ideal feature；Accuracy of identification can be improved according to spatial domain and transform domain double check.
Description
Technical field
The present invention relates to parasite egg pictures identifications, and in particular to parasite egg pictures recognition methods.
Background technique
Parasite of human refers to the helminth using people as host.Endophyte worm and epizoon two major classes can be divided into.
Belong to protozoan, round worm, flatworm, annelid and arthropod mostly.Traditionally primary dynamic in parasitology
Object is known as protozoon class, and round worm and flatworm are collectively referred to as Vermes.The important type of endophyte worm includes mostly
In protozoon class, Nemata, Trematoda and tapeworms.Studying parasite egg has important meaning to medical students ' learning and guidance
Justice.Parasite egg classification, which exists, at present identifies that difficult, otherness is small, there are problems that being difficult to when interference.
Summary of the invention
Exist the technical problem to be solved by the present invention is to current parasite egg classification and identify that difficult, otherness is small, deposits
The problem of being difficult in interference is, and it is an object of the present invention to provide parasite egg pictures recognition methods, solves the above problems.
The present invention is achieved through the following technical solutions:
Parasite egg pictures recognition methods, including parasite egg classification sample, images to be recognized, further include following step
It is rapid:
S1 obtains the images to be recognized from parasite egg collection point, carries out decomposition field transformation in the spatial domain, makes figure
In the multiple scale spaces decomposed as information；
S2 carries out the edge detection of images to be recognized using spatial domain gradient operator；
S3 carries out the edge detection of images to be recognized using small echo in image transform domain；
S4 extracts the feature vector for the images to be recognized that edge detection obtains in spatial domain and transform domain；
S5, carries out pattern-recognition using artificial neural network, and parasite egg classification sample is carried out off-line training, is determined
Weight carries out operation with the feature vector that S4 is obtained, realizes the identification of images to be recognized.
Further, the decomposition field transformation in the S1 is converted using Multiscale Wavelet Decomposition domain.
Further, the gradient operator in the S2 uses Gauss-Laplace.
Further, the edge detection results in the S3 are recorded using chained list.
Further, the feature vector in the S4 uses statistical nature.
Compared with prior art, the present invention having the following advantages and benefits:
1, parasite egg pictures recognition methods of the present invention can effectively extract the edge of parasite egg, as ideal
Feature；
2, parasite egg pictures recognition methods of the present invention can improve and know according to spatial domain and transform domain double check
Other precision.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below with reference to embodiment, the present invention is made
Further to be described in detail, exemplary embodiment of the invention and its explanation for explaining only the invention, are not intended as to this
The restriction of invention.
Embodiment
Parasite egg pictures recognition methods of the present invention, including parasite egg classification sample, images to be recognized further include
Following steps:
S1 obtains the images to be recognized from parasite egg collection point, carries out decomposition field transformation in the spatial domain, makes figure
In the multiple scale spaces decomposed as information；
S2 carries out the edge detection of images to be recognized using spatial domain gradient operator；
S3 carries out the edge detection of images to be recognized using small echo in image transform domain；
S4 extracts the feature vector for the images to be recognized that edge detection obtains in spatial domain and transform domain；
S5, carries out pattern-recognition using artificial neural network, and parasite egg classification sample is carried out off-line training, is determined
Weight carries out operation with the feature vector that S4 is obtained, realizes the identification of images to be recognized.
Decomposition field transformation in the S1 is converted using Multiscale Wavelet Decomposition domain.
Gradient operator in the S2 uses Gauss-Laplace.
Edge detection results in the S3 are recorded using chained list.
Feature vector in the S4 uses statistical nature.
Above-described specific embodiment has carried out further the purpose of the present invention, technical scheme and beneficial effects
It is described in detail, it should be understood that being not intended to limit the present invention the foregoing is merely a specific embodiment of the invention
Protection scope, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should all include
Within protection scope of the present invention.
Claims (5)
1. parasite egg pictures recognition methods, including parasite egg classification sample, images to be recognized, which is characterized in that also
The following steps are included:
S1 obtains the images to be recognized from parasite egg collection point, carries out decomposition field transformation in the spatial domain, believes image
It ceases in the multiple scale spaces decomposed；
S2 carries out the edge detection of images to be recognized using spatial domain gradient operator；
S3 carries out the edge detection of images to be recognized using small echo in image transform domain；
S4 extracts the feature vector for the images to be recognized that edge detection obtains in spatial domain and transform domain；
S5, carries out pattern-recognition using artificial neural network, and parasite egg classification sample is carried out off-line training, determines power
Value carries out operation with the feature vector that S4 is obtained, realizes the identification of images to be recognized.
2. parasite egg pictures recognition methods according to claim 1, which is characterized in that the decomposition field in the S1 becomes
It changes and is converted using Multiscale Wavelet Decomposition domain.
3. parasite egg pictures recognition methods according to claim 1, which is characterized in that the gradient operator in the S2
Using Gauss-Laplace.
4. parasite egg pictures recognition methods according to claim 1, which is characterized in that the edge detection in the S3
As a result it is recorded using chained list.
5. parasite egg pictures recognition methods according to claim 1, which is characterized in that the feature vector in the S4
Using statistical nature.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810487123.0A CN110516505A (en) | 2018-05-21 | 2018-05-21 | Parasite egg pictures recognition methods |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810487123.0A CN110516505A (en) | 2018-05-21 | 2018-05-21 | Parasite egg pictures recognition methods |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110516505A true CN110516505A (en) | 2019-11-29 |
Family
ID=68621989
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810487123.0A Withdrawn CN110516505A (en) | 2018-05-21 | 2018-05-21 | Parasite egg pictures recognition methods |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110516505A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111582276A (en) * | 2020-05-29 | 2020-08-25 | 北京语言大学 | Parasite egg identification method and system based on multi-feature fusion |
-
2018
- 2018-05-21 CN CN201810487123.0A patent/CN110516505A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111582276A (en) * | 2020-05-29 | 2020-08-25 | 北京语言大学 | Parasite egg identification method and system based on multi-feature fusion |
| CN111582276B (en) * | 2020-05-29 | 2023-09-29 | 北京语言大学 | Recognition method and system for parasite eggs based on multi-feature fusion |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105809693B (en) | SAR image registration method based on deep neural network | |
| Wu et al. | Unsupervised deep feature learning for deformable registration of MR brain images | |
| Nasir et al. | Segmentation based approach for detection of malaria parasites using moving k-means clustering | |
| CN104700078B (en) | A kind of robot scene recognition methods based on scale invariant feature extreme learning machine | |
| CN112001217A (en) | Multi-person human body posture estimation algorithm based on deep learning | |
| CN106446804A (en) | ELM-based multi-granularity iris recognition method | |
| CN110516505A (en) | Parasite egg pictures recognition methods | |
| Osman et al. | Segmentation of tuberculosis bacilli in ziehl-neelsen tissue slide images using hibrid multilayered perceptron network | |
| Hussain et al. | Face recognition using multiscale and spatially enhanced weber law descriptor | |
| CN109101525A (en) | A kind of medical image comparison method based on image comparison identification | |
| Mishra | Application Specific Instrumentation (ASIN): A Bio-inspired Paradigm to Instrumentation using recognition before detection | |
| CN104077564B (en) | Iris pupil takes turns area information extracting method to crispaturaing | |
| Suganthy et al. | Effective iris recognition for security enhancement | |
| CN110516508A (en) | A method of improving fingerprint recognition accuracy rate | |
| Ghosh et al. | Content based retrieval of malaria positive images from a clinical database via recognition in RGB colour space | |
| CN110503107A (en) | Parasite egg pictures feature selection approach | |
| CN110516507A (en) | A kind of stationary source fugitive dust classification knowledge method for distinguishing | |
| Pho et al. | Protozoa identification using 3D geometric multiple color channel local feature | |
| Ali et al. | Iris identification system based on RLM texture features | |
| Zhang et al. | A fusion iris feature extraction method based on fisher linear discriminant | |
| Dizaji et al. | Diagnosing interdental decays in mouth radiography images using Kernel Fuzzy C means segmentation and cascade object detector | |
| Taguchi et al. | Validation of the Role of Attention Mechanism in Predicting Brain Activity | |
| James et al. | PLANT DETECTION USING DEEP LEARNING | |
| CN110516682A (en) | Image-recognizing method for the positioning of sewage Tou Pai enterprise | |
| Cherabit et al. | A robust iris localization method of facial faces |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20191129 |
|
| WW01 | Invention patent application withdrawn after publication |