CN107978185B - A kind of good children learning machine of teaching efficiency - Google Patents
A kind of good children learning machine of teaching efficiency Download PDFInfo
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- CN107978185B CN107978185B CN201711288539.1A CN201711288539A CN107978185B CN 107978185 B CN107978185 B CN 107978185B CN 201711288539 A CN201711288539 A CN 201711288539A CN 107978185 B CN107978185 B CN 107978185B
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- 238000011156 evaluation Methods 0.000 claims description 54
- 238000005352 clarification Methods 0.000 claims description 7
- 238000000605 extraction Methods 0.000 claims description 7
- 238000000034 method Methods 0.000 claims description 7
- 230000011218 segmentation Effects 0.000 claims description 7
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 5
- 238000001914 filtration Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 2
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Classifications
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B5/00—Electrically-operated educational appliances
- G09B5/06—Electrically-operated educational appliances with both visual and audible presentation of the material to be studied
- G09B5/065—Combinations of audio and video presentations, e.g. videotapes, videodiscs, television systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/16—Sound input; Sound output
- G06F3/167—Audio in a user interface, e.g. using voice commands for navigating, audio feedback
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- G06T5/70—
-
- 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
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V10/757—Matching configurations of points or features
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- 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
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
-
- 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
- G06T2207/20172—Image enhancement details
- G06T2207/20192—Edge enhancement; Edge preservation
Abstract
The present invention provides a kind of good children learning machines of teaching efficiency, including camera, identification device, display module and voice module, the camera is used to acquire the image of interesting target, the identification device is for identifying the target in image, the display module is for showing the recognition result, the display module is high-definition display screen, and the voice module is used to carry out voice broadcast to recognition result.The invention has the benefit that children can learn interested target by learning machine, the desire of children for learning is excited, good teaching efficiency has been reached.
Description
Technical field
The present invention relates to children learning machine technical fields, and in particular to a kind of good children learning machine of teaching efficiency.
Background technique
Existing children learning machine for the abundant in content colorful of study, but it is most of be all learning machine storage inside text
This information and pictorial information, lack acquisition capability and recognition capability to extraneous target, and children can not explore the world.
There are many method of image recognition, can generally be summarized as statistical picture identification, structural images identification, fuzzy set figure
As identification, images match identification.Images match is the wherein most representative, method that is most widely used, in moving target
The fields such as tracking, remote sensing images identification, robot vision have all been applied, but existing recognition methods recognition performance is poor,
It can not effectively be identified.
Summary of the invention
In view of the above-mentioned problems, the present invention is intended to provide a kind of good children learning machine of teaching efficiency.
The purpose of the present invention is realized using following technical scheme:
Provide a kind of good children learning machine of teaching efficiency, including camera, identification device, display module and voice
Module, the camera are used to acquire the image of interesting target, and the identification device is for knowing the target in image
Not, the display module is high-definition display screen for showing the recognition result, the display module, and the voice module is used for
Voice broadcast is carried out to recognition result.
The invention has the benefit that children can learn interested target by learning machine, excite
The desire of child's study, has reached good teaching efficiency.
Detailed description of the invention
The present invention will be further described with reference to the accompanying drawings, but the embodiment in attached drawing is not constituted to any limit of the invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is structural schematic diagram of the invention；
Appended drawing reference:
Camera 1, identification device 2, display module 3, voice module 4.
Specific embodiment
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of good children learning machine of teaching efficiency of the present embodiment, including camera 1, identification device 2,
Display module 3 and voice module 4, the camera 1 are used to acquire the image of interesting target, the identification device 2 for pair
Target in image is identified that the display module 3 is aobvious for high definition for showing the recognition result, the display module 3
Display screen, the voice module 4 are used to carry out voice broadcast to recognition result.
The present embodiment children can learn interested target by learning machine, excite the desire of children for learning
It hopes, has reached good teaching efficiency.
Preferably, the identification device 2 includes the first image pre-processing module, second feature extraction module, third image
Identification module and the 4th identification and evaluation module, the first image preprocessing module are used to pre-process the image of acquisition,
The second feature extraction module obtains target feature vector for extracting to pretreated characteristics of image, and described the
Three picture recognition modules are matched according to target feature vector and target template, are identified to target, the 4th identification
Evaluation module is for evaluating the recognition performance of third picture recognition module.
The present embodiment realize children learning machine to target accurately identify and the evaluation of recognition performance.
Preferably, the first image preprocessing module includes object segmentation unit and pretreatment unit, the target point
It cuts unit to extract the outer edge of target in image using Canny operator, by Target Segmentations multiple in image at single mesh
Logo image, the pretreatment unit carry out greyscale transformation and filtering processing to simple target image.
For difference between different target, this preferred embodiment object segmentation unit is by the Segmentation of Multi-target in image at more
A simple target pre-processes each target image, is convenient for subsequent extracted different target feature；For in Image Acquisition mistake
Cheng Zhong is inevitably mixed into random noise by various interference, this preferred embodiment pretreatment unit carries out ash to image
Degreeization and filtering processing help to reduce identification error, preferably keep the marginal information of target.
Preferably, the second feature extraction module includes single treatment unit, secondary treatment unit and handles three times single
Member, the single treatment unit are used to extract the fisrt feature of target, and the secondary treatment unit is for extracting the second of target
Feature, the processing unit three times generate clarification of objective vector according to fisrt feature and second feature.
The single treatment unit is used to extract the fisrt feature of target, specifically: the outer edge for extracting target obtains
The coordinate of target outer profile pixel and each Internal periphery pixel, the fisrt feature of target are as follows: EH1=[EM, YW1,…,YWL]；
In formula, EH1Indicate the fisrt feature of target, EM indicates the characteristic value of target outer profile, YWl(l=1,2 ...,
L the characteristic value of first of Internal periphery of target) is indicated, L indicates the number of the Internal periphery of target；
The characteristic value of the target outer profile obtains in the following manner: binary conversion treatment is carried out to image, by outer profile
Grey scale pixel value is denoted as 1, remaining position grey scale pixel value is denoted as 0；Calculate the characteristic value EM of target outer profile:
In formula, I (i, j) indicates that pixel position is the gray value of (i, j), and n and m respectively indicate the width of target image
And height, indicate target outer profile number of pixels；
The characteristic value of the target outer profile obtains in the following manner: binary conversion treatment is carried out to image, by target l
The grey scale pixel value of a Internal periphery is denoted as 1, remaining position grey scale pixel value is denoted as 0；Calculate the characteristic value of first of Internal periphery of target
YWl:
The secondary treatment unit is used to extract the second feature of target, specifically: the outer edge for extracting target obtains target
Outer profile inclusion region pixel, to image carry out binary conversion treatment, the grey scale pixel value of outer profile inclusion region is denoted as 1,
Remaining position grey scale pixel value is denoted as 0, calculates the Second Eigenvalue of target:
In formula, EH2Indicate the second feature of target；
The processing unit three times generates clarification of objective vector according to fisrt feature and second feature, specifically: by mesh
Target fisrt feature and second feature constitutive characteristic vector: EH=[EH1,EH2], in formula, EH indicate clarification of objective to
Amount.
This preferred embodiment extracts target signature by second feature extraction module, matches and knows for succeeding target
It does not lay a good foundation, by establishing clarification of objective vector, more accurate identification can be carried out to target, specifically, first
Feature has fully considered the outer profile and Internal periphery of target, and second feature has fully considered whole pixel in outer profile, and target is special
Sign vector combines fisrt feature and second feature, has obtained more complete target signature.
Preferably, the 4th identification and evaluation module includes the first identification and evaluation unit, the second identification and evaluation unit and comprehensive
Identification and evaluation unit is closed, the first identification and evaluation unit is used to obtain the first evaluation of estimate of recognition performance, second identification
Evaluation unit is used to obtain the second evaluation of estimate of recognition performance, the comprehensive identification and evaluation unit be used for according to the first evaluation of estimate and
Second evaluation of estimate carries out overall merit to target identification performance；
The first identification and evaluation unit is used to obtain the first evaluation of estimate of recognition performance, specifically:
In formula, CS1Indicate the first evaluation of estimate, ZC indicates the target numbers for including in image, ZC1Expression can identify
Target number, ZC2Indicate the target number correctly identified；
The second identification and evaluation unit is used to obtain the second evaluation of estimate of recognition performance, specifically:
In formula, CS2Indicate the second evaluation of estimate, FS indicates picture number to be identified, FS1The first evaluation of estimate indicated
Greater than the picture number of given threshold；
The comprehensive identification and evaluation unit is used to carry out target identification performance according to the first evaluation of estimate and the second evaluation of estimate
Overall merit: the overall merit factor is calculated:
In formula, CS indicates the overall merit factor；The overall merit factor is bigger, indicates that target identification performance is better.
This preferred embodiment realizes the evaluation of third picture recognition module recognition performance by the 4th identification and evaluation module,
It ensure that target identification level, specifically, the first evaluation of estimate considers the identification accuracy of target, the second evaluation of estimate considers mesh
Target identifies that stability, target identification calculate the overall merit factor by the first evaluation of estimate and the second evaluation of estimate, can integrate
Reflect recognition performance, to ensure that the teaching efficiency of learning machine.
Children are learnt using the good children learning machine of teaching efficiency of the present invention, are chosen 5 children and are tested, point
Not Wei children 1, children 2, children 3, children 4, children 5, learning efficiency and children's satisfaction are counted, compared with children learn
Habit machine is compared, and generation has the beneficial effect that shown in table:
| Learning efficiency improves | Children's satisfaction improves | |
| Children 1 | 29% | 27% |
| Children 2 | 27% | 26% |
| Children 3 | 26% | 26% |
| Children 4 | 25% | 24% |
| Children 5 | 24% | 22% |
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although explaining in detail referring to preferred embodiment to the present invention, those skilled in the art are answered
Work as understanding, it can be with modification or equivalent replacement of the technical solution of the present invention are made, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (4)
1. a kind of good children learning machine of teaching efficiency, which is characterized in that including camera, identification device, display module and
Voice module, the camera are used to acquire the image of interesting target, the identification device be used for the target in image into
Row identification, the display module are high-definition display screen, the voice module for showing the recognition result, the display module
For carrying out voice broadcast to recognition result, the identification device includes the first image pre-processing module, second feature extraction mould
Block, third picture recognition module and the 4th identification and evaluation module, the first image preprocessing module are used for the image to acquisition
It is pre-processed, the second feature extraction module obtains target signature for extracting to pretreated characteristics of image
Vector, the third picture recognition module are matched according to target feature vector and target template, are identified to target, institute
The 4th identification and evaluation module is stated for evaluating the recognition performance of third picture recognition module, the first image pre-processes
Module includes object segmentation unit and pretreatment unit, and the object segmentation unit is using Canny operator to target in image
Outer edge extracts, and by Target Segmentations multiple in image at simple target image, the pretreatment unit is to simple target figure
As carrying out greyscale transformation and filtering processing, the second feature extraction module include single treatment unit, secondary treatment unit and
Processing unit three times, the single treatment unit are used to extract the fisrt feature of target, and the secondary treatment unit is for extracting
The second feature of target, the processing unit three times generate clarification of objective vector according to fisrt feature and second feature；
The single treatment unit is used to extract the fisrt feature of target, specifically: the outer edge for extracting target obtains target
The coordinate of outer profile pixel and each Internal periphery pixel, the fisrt feature of target are as follows: EH1=[EM, YW1..., YWL]；
In formula, EH1Indicate the fisrt feature of target, EM indicates the characteristic value of target outer profile, YWl(l=1,2 ..., L) table
Show the characteristic value of first of Internal periphery of target, L indicates the number of the Internal periphery of target；
The characteristic value of the target outer profile obtains in the following manner: binary conversion treatment is carried out to image, by outer profile pixel
Gray value is denoted as 1, remaining position grey scale pixel value is denoted as 0；Calculate the characteristic value EM of target outer profile:
In formula, I (i, j) indicates that pixel position is the gray value of (i, j), n and m respectively indicate target image width and
Height indicates target outer profile number of pixels；
The characteristic value of the target outer profile obtains in the following manner: binary conversion treatment is carried out to image, it will be in first of target
The grey scale pixel value of profile is denoted as 1, remaining position grey scale pixel value is denoted as 0；Calculate the characteristic value YW of first of Internal periphery of targetl:
The secondary treatment unit is used to extract the second feature of target, specifically: the outer edge for extracting target obtains target
The pixel of outer profile inclusion region carries out binary conversion treatment to image, the grey scale pixel value of outer profile inclusion region is denoted as 1,
Remaining position grey scale pixel value is denoted as 0, calculates the Second Eigenvalue of target:
In formula, EH2Indicate the second feature of target.
2. the good children learning machine of teaching efficiency according to claim 1, which is characterized in that the processing unit three times
Clarification of objective vector is generated according to fisrt feature and second feature, specifically: by the fisrt feature of target and second feature structure
At feature vector: EH=[EH1, EH2], in formula, EH indicates clarification of objective vector.
3. the good children learning machine of teaching efficiency according to claim 2, which is characterized in that the 4th identification and evaluation
Module includes the first identification and evaluation unit, the second identification and evaluation unit and comprehensive identification and evaluation unit, first identification and evaluation
Unit is used to obtain the first evaluation of estimate of recognition performance, and the second identification and evaluation unit is commented for obtaining the second of recognition performance
Value, the comprehensive identification and evaluation unit is for integrating target identification performance according to the first evaluation of estimate and the second evaluation of estimate
Evaluation.
4. the good children learning machine of teaching efficiency according to claim 3, which is characterized in that first identification and evaluation
Unit is used to obtain the first evaluation of estimate of recognition performance, specifically:
In formula, CS1Indicate the first evaluation of estimate, ZC indicates the target numbers for including in image, ZC1Indicate the mesh that can be identified
Mark number, ZC2Indicate the target number correctly identified；
The second identification and evaluation unit is used to obtain the second evaluation of estimate of recognition performance, specifically:
In formula, CS2Indicate the second evaluation of estimate, FS indicates picture number to be identified, FS1The first evaluation of estimate indicated is greater than
The picture number of given threshold；
The comprehensive identification and evaluation unit is for integrating target identification performance according to the first evaluation of estimate and the second evaluation of estimate
Evaluation: the overall merit factor is calculated:
In formula, CS indicates the overall merit factor；The overall merit factor is bigger, indicates that target identification performance is better.
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| CN104751108A (en) * | 2013-12-31 | 2015-07-01 | 汉王科技股份有限公司 | Face image recognition device and face image recognition method |
| CN105512664A (en) * | 2015-12-03 | 2016-04-20 | 小米科技有限责任公司 | Image recognition method and device |
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| CN106991668A (en) * | 2017-03-09 | 2017-07-28 | 南京邮电大学 | A kind of evaluation method of day net camera shooting picture |
| CN107403168A (en) * | 2017-08-07 | 2017-11-28 | 青岛有锁智能科技有限公司 | A kind of facial-recognition security systems |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104751108A (en) * | 2013-12-31 | 2015-07-01 | 汉王科技股份有限公司 | Face image recognition device and face image recognition method |
| CN105512664A (en) * | 2015-12-03 | 2016-04-20 | 小米科技有限责任公司 | Image recognition method and device |
| CN105913433A (en) * | 2016-04-12 | 2016-08-31 | 北京小米移动软件有限公司 | Information pushing method and information pushing device |
| CN106991668A (en) * | 2017-03-09 | 2017-07-28 | 南京邮电大学 | A kind of evaluation method of day net camera shooting picture |
| CN107403168A (en) * | 2017-08-07 | 2017-11-28 | 青岛有锁智能科技有限公司 | A kind of facial-recognition security systems |
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