CN108052868A - A kind of identifying system and method for the passage difference binaryzation based on BP neural network - Google Patents
A kind of identifying system and method for the passage difference binaryzation based on BP neural network Download PDFInfo
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Abstract
The invention discloses a kind of identifying system and method for the passage difference binaryzation based on BP neural network, identifying system includes image capture module, data transmission module and central processing unit；Using the method for the identifying system, by way of based on passage difference and OTSU algorithms carry out binarization segmentation to image, in conjunction in morphology closed operation, seed fill algorithm obtain preferable tennis profile, on this basis using the characteristic value structure BP neural network of tennis, so as to efficiently identify out tennis.The identifying system and method for the present invention, can not only accurately be identified tennis, and the processing method identified is simple, and to the of less demanding of data processing platform (DPP), equipment cost is low, and software development kit is increased income, therefore preferably can be promoted and be used.
Description
Technical field
The present invention relates to intelligent robot identification technology field, especially a kind of passage difference two based on BP neural network
The identifying system and method for value.
Background technology
With the development and progress of society, tennis attracts more and more people with its unique glamour and participates in, but
Pickup to tennis is annoying, time-consuming work, to improve tennis training effect, increasing the enjoyment of the project and meeting market need
It asks, it is more and more urgent to the correlative study work of tennis pickup robot.
Existing tennis identification technology, is typically realized by the way of sensor instrument distance, such as is passed using Kinect
Sensor obtains distance and opposite deviation angle of the tennis for being distributed in court different position apart from tennis pickup robot respectively, from
And tennis pickup robot is enabled accurately to pick up tennis.But the processing method of this tennis identification is sufficiently complex, to data
Processing platform it is more demanding, equipment cost is also high, and software development kit is partly increases income, therefore is unfavorable for promoting and use.
The content of the invention
To solve the above problems, it is an object of the invention to provide a kind of passage difference binaryzations based on BP neural network
Identifying system and method, accurately tennis can not only be identified, and identify processing method it is simple, to data processing
Platform it is of less demanding, equipment cost is low, and software development kit is increased income, therefore preferably can be promoted and be used.
Technical solution is used by the present invention solves the problems, such as it：
A kind of identifying system of the passage difference binaryzation based on BP neural network, including being used to capture original image f0's
Image capture module, the passage difference two-value of the data transmission module for being used for transmission image data and utilization based on BP neural network
Change the central processing unit for image data being identified processing；Image capture module is connected with data transmission module, and data pass
Defeated module carries out data interaction with central processing unit；Image capture module, data transmission module and central processing unit cooperate
The identification and positioning to tennis are realized using the method for machine vision.
Further, image capture module includes camera, camera control chip and power management chip, data transmission mould
Block is wireless router, and camera, camera control chip and wireless router are sequentially connected, power management chip and video camera
Control chip is connected.
Using a kind of method of the identifying system of the passage difference binaryzation based on BP neural network, comprise the following steps：
A, image preprocessing is carried out to the original image f0 collected by image capture module；
B, original image f0 is split using the binarization segmentation algorithm based on passage difference, obtains binary image f0 ', and
Judge to whether there is tennis among original image f0；
C, to binary image f0 ' carry out profiles hole-filling, profile separation and noise filtering；
D, the characteristic value of tennis is extracted among binary image f0 '；
E, BP neural network is built according to the characteristic value of tennis, tennis is determined whether according to BP neural network.
Further, image preprocessing is carried out to the original image f0 collected by image capture module in step A, for original
Beginning image f0 carries out gaussian filtering.
Further, using the binarization segmentation algorithm segmentation original image f0 based on passage difference in step B, including following
Step：
B1, Color Channel separation is carried out to original image f0；
B2, single channel image is obtained according to the difference of the pixel value between the r channel images of original image f0 and g channel images
f1；
B3, using OTSU algorithms to single channel image f1 into row threshold division, obtain binary image f0 '.
Further, judge to whether there is tennis among original image f0 in step B, when the threshold value being obtained using OTSU algorithms
During more than 20, image segmentation is carried out to single channel image f1 using the threshold value, and is judged among original image f0 there are tennis, it is no
Then judge that there is no tennises among original image f0.
Further, binary image f0 ' carry out profiles hole-filling, profile separation and noise filtering are used in step C
Closed operation and seed fill algorithm in morphology separate binary image f0 ' carry out profile hole-fillings or profile, use
Area-method is to binary image f0 ' carry out noise filterings.
Further, the characteristic value of tennis, including contour area X0, the contour area extracted from binary image f0 '
X0 is with ratio X 1=η of its external area of a circle, the Y-coordinate X2 in the circumscribed circle center of circle, the coordinate in the circumscribed circle center of circle in HSV space
H component values X3 and the coordinate in the circumscribed circle center of circle are in the S component values X4 of HSV space.
The beneficial effects of the invention are as follows：A kind of identifying system of passage difference binaryzation based on BP neural network and side
Method, image capture module can clearly capture original image f0 exactly, and data transmission module can be exactly original image
F0 is transferred to central processing unit, and central processing unit then can carry out image procossing and identification to original image f0；Central processing
When device carries out original image f0 image procossing and identification, first by the way of based on passage difference and OTSU algorithms are to original
Image f0 carries out binaryzation, then builds BP neural network according to the characteristic value of tennis on this basis, finally completes to tennis
Effective identification.The identifying system of the present invention, the cost formed are low；And the method for applying the identifying system, the place of identification
Reason method is simple, and the algorithm arrived used in it, is the algorithm increased income, and the requirement to data processing platform (DPP) is not
Height, therefore preferably can be promoted and be used.In addition, the method for the present invention can be under the disturbance of complicated light, effectively
The identification for target is completed, while this method is expected to be applied to other machine vision scenes such as industry, business.
Description of the drawings
The invention will be further described with example below in conjunction with the accompanying drawings.
Fig. 1 is the schematic diagram of identifying system；
Fig. 2 is the flow chart of the method for application identification system.
Specific embodiment
With reference to Fig. 1, the identifying system of a kind of passage difference binaryzation based on BP neural network of the invention, including being used for
The image capture module 1 of capture original image f0, the data transmission module 2 for being used for transmission image data and utilization are based on BP nerves
Image data is identified the central processing unit 3 of processing in the passage difference binaryzation of network；Image capture module 1 is passed with data
Defeated module 2 is connected, and data transmission module 2 carries out data interaction with central processing unit 3；Image capture module 1, data transmission mould
Block 2 and central processing unit 3, which cooperate, utilizes identification and positioning of the method realization of machine vision to tennis.Wherein, image is adopted
Collecting module 1 includes camera 11, camera control chip 12 and power management chip 13, and data transmission module 2 is wireless routing
Device, camera 11, camera control chip 12 and wireless router are sequentially connected, power management chip 13 and camera control core
Piece 12 is connected.Specifically, when central processing unit 3 carries out data processing to original image f0, the centre bit of tennis can be calculated
It puts and circumradius, so as to identify that tennis provides necessary processing parameter.
With reference to Fig. 1-Fig. 2, using a kind of method of the identifying system of the passage difference binaryzation based on BP neural network,
Comprise the following steps：
A, image preprocessing is carried out to the original image f0 collected by image capture module 1；
B, original image f0 is split using the binarization segmentation algorithm based on passage difference, obtains binary image f0 ', and
Judge to whether there is tennis among original image f0；
C, to binary image f0 ' carry out profiles hole-filling, profile separation and noise filtering；
D, the characteristic value of tennis is extracted among binary image f0 '；
E, BP neural network is built according to the characteristic value of tennis, tennis is determined whether according to BP neural network.
Specifically, when central processing unit 3 carries out original image f0 image procossing and identification, first using based on passage difference
Mode and OTSU algorithms to original image f0 carry out binaryzation, then on this basis according to the characteristic value of tennis build BP
Neutral net finally completes effective identification to tennis.Therefore, recognition methods of the invention, the processing method of identification is simple,
And the algorithm arrived used in it is the algorithm increased income, and to the of less demanding of data processing platform (DPP), and use this
The identifying system of method, the cost formed is low, therefore preferably can be promoted and be used.In addition, the identification of the present invention
System and method can effectively complete the identification for target, while the recognition methods is expected to answer under the disturbance of complicated light
For other machine vision scenes such as industry, business.
Wherein, image is carried out to the original image f0 collected by image capture module 1 in reference Fig. 1-Fig. 2, step A
Pretreatment, to carry out gaussian filtering to original image f0.
Wherein, using the binarization segmentation algorithm segmentation original image based on passage difference in reference Fig. 1-Fig. 2, step B
F0 comprises the following steps：
B1, Color Channel separation is carried out to original image f0；
B2, single channel image is obtained according to the difference of the pixel value between the r channel images of original image f0 and g channel images
f1；
B3, using OTSU algorithms to single channel image f1 into row threshold division, obtain binary image f0 '.
Wherein, judge to whether there is tennis among original image f0 in reference Fig. 1-Fig. 2, step B, be calculated when using OTSU
The threshold value that method is obtained be more than 20 when, using the threshold value to single channel image f1 carry out image segmentation, and judge original image f0 it
In there are tennis, otherwise judge that there is no tennises among original image f0.Specifically, ground according to the analysis of passage differential data
Study carefully and understand, in differentiated single channel image, the pixel value of tennis is all higher than 20, therefore, when the threshold being obtained using OTSU algorithms
When value is more than 20, illustrate there are tennis among current image, in order to which further tennis is identified, so as to single channel figure
As f1 carries out image dividing processing.
Wherein, binary image f0 ' carry out profiles hole-filling, profile are separated and made an uproar in reference Fig. 1-Fig. 2, step C
Sound filters out, using the closed operation in morphology and seed fill algorithm to binary image f0 ' carry out profile hole-fillings or wheel
Exterior feature separation, using face area method is to binary image f0 ' carry out noise filterings.Specifically, there can be wheel among binary image f0 '
The wide internal situation for having adhesion between cavity or profile, it is therefore desirable to utilize the closed operation in morphology and seed fill algorithm pair
Binary image f0 ' carry out profile hole-fillings and profile separating treatment；In addition, it still suffers from being permitted among binary image f0 '
The larger noise of many areas, it is therefore desirable to using area-method to binary image f0 ' carry out noise filtering processing.
Wherein, with reference to Fig. 1-Fig. 2, the characteristic value of tennis, including the contour area extracted from binary image f0 '
X0, contour area X0 and ratio X 1=η of its external area of a circle, the Y-coordinate X2 in the circumscribed circle center of circle, the circumscribed circle center of circle coordinate in
The coordinate of H component values X3 and the circumscribed circle center of circle in HSV space are in the S component values X4 of HSV space.Specifically, net can be identified
The characteristic value of ball has 5, is respectively ratio X 1=η, external round of contour area X0, contour area X0 and its external area of a circle
The coordinate of H component value X3s and the circumscribed circle center of circle of the Y-coordinate X2, the coordinate in the circumscribed circle center of circle of the heart in HSV space are in HSV space
S component value X4, therefore by the way that this 5 characteristic values are built into new BP neural network and are trained, then two-value
Change image f0 ' to be input among the BP neural network after training, and then can calculate whether present image is tennis.
Therefore, recognition methods of the invention can exactly be identified tennis, and the processing method identified is simple, to data
Processing platform it is of less demanding, therefore preferably can be promoted and be used.
The recognition methods of the present embodiment carries out gaussian filtering to image first, will treated picture by being based on passage
The mode and OTSU algorithms of difference carry out binarization segmentation to image, in conjunction with closed operation, the seed filling calculation in morphology
Method obtains preferable tennis profile, then builds new BP neural network using 5 characteristic values of tennis on this basis, so as to
Tennis can be efficiently identified out, obtains the central coordinate of circle and radius of tennis.The identifying system and method for the present invention, can be applicable to
In the machinery equipment for picking up tennis, the position where energy automatic identification tennis thus allows for automatic Picking.
The above are implementing to be illustrated to the preferable of the present invention, but the invention is not limited in above-mentioned embodiment party
Formula, those skilled in the art can also make a variety of equivalent variations on the premise of without prejudice to spirit of the invention or replace
It changes, these equivalent deformations or replacement are all contained in the application claim limited range.
Claims (8)
1. a kind of identifying system of the passage difference binaryzation based on BP neural network, it is characterised in that：Including being used to capture original
The image capture module (1) of beginning image f0, the data transmission module (2) for being used for transmission image data and utilization are based on BP nerve nets
Image data is identified the central processing unit (3) of processing in the passage difference binaryzation of network；Described image acquisition module (1) with
The data transmission module (2) is connected, and the data transmission module (2) carries out data interaction with the central processing unit (3)；
The method that described image acquisition module (1), data transmission module (2) and central processing unit (3) cooperate using machine vision
Realize the identification and positioning to tennis.
2. a kind of identifying system of passage difference binaryzation based on BP neural network according to claim 1, feature
It is：Described image acquisition module (1) includes camera (11), camera control chip (12) and power management chip (13),
The data transmission module (2) be wireless router, the camera (11), camera control chip (12) and wireless router
It is sequentially connected, the power management chip (13) is connected with camera control chip (12).
3. a kind of identifying system of any passage difference binaryzations based on BP neural network of application claim 1-2
Method, it is characterised in that：Comprise the following steps：
A, image preprocessing is carried out to the original image f0 collected by image capture module (1)；
B, original image f0 is split using the binarization segmentation algorithm based on passage difference, obtains binary image f0 ', and judge
It whether there is tennis among original image f0；
C, to binary image f0 ' carry out profiles hole-filling, profile separation and noise filtering；
D, the characteristic value of tennis is extracted among binary image f0 '；
E, BP neural network is built according to the characteristic value of tennis, tennis is determined whether according to BP neural network.
4. according to the method described in claim 3, it is characterized in that：To being collected by image capture module (1) in the step A
Original image f0 carry out image preprocessing, for original image f0 carry out gaussian filtering.
5. according to the method described in claim 3, it is characterized in that：The binaryzation based on passage difference is used in the step B
Partitioning algorithm splits original image f0, comprises the following steps：
B1, Color Channel separation is carried out to original image f0；
B2, single channel image f1 is obtained according to the difference of the pixel value between the r channel images of original image f0 and g channel images；
B3, using OTSU algorithms to single channel image f1 into row threshold division, obtain the binary image f0 '.
6. according to the method described in claim 5, it is characterized in that：Judge whether deposited among original image f0 in the step B
In tennis, when the threshold value being obtained using OTSU algorithms is more than 20, image segmentation is carried out to single channel image f1 using the threshold value,
And judge otherwise to judge that there is no tennises among original image f0 there are tennis among original image f0.
7. according to the method described in claim 3, it is characterized in that：It is empty to binary image f0 ' carry out profile in the step C
Hole is filled up, profile separates and noise filtering, using the closed operation in morphology and seed fill algorithm to binary image f0 ' into
Row profile hole-filling or profile separation, using face area method is to binary image f0 ' carry out noise filterings.
8. according to the method described in claim 3, it is characterized in that：The characteristic value of the tennis, including from binary image f0 '
In the contour area X0, the contour area X0 that extract sat with ratio X 1=η of its external area of a circle, the Y in the circumscribed circle center of circle
The coordinate of the H component values X3 and the circumscribed circle center of circle of X2, the coordinate in the circumscribed circle center of circle in HSV space are marked in the S components of HSV space
Value X4.
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| CN111292338A (en) * | 2020-01-22 | 2020-06-16 | 苏州大学 | Method and system for segmenting choroidal neovascularization from fundus OCT image |
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| CN111292338A (en) * | 2020-01-22 | 2020-06-16 | 苏州大学 | Method and system for segmenting choroidal neovascularization from fundus OCT image |
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