CN110163883A - A kind of tensioning integrated wheel type train of mechanism - Google Patents
A kind of tensioning integrated wheel type train of mechanism Download PDFInfo
- Publication number
- CN110163883A CN110163883A CN201810275205.9A CN201810275205A CN110163883A CN 110163883 A CN110163883 A CN 110163883A CN 201810275205 A CN201810275205 A CN 201810275205A CN 110163883 A CN110163883 A CN 110163883A
- Authority
- CN
- China
- Prior art keywords
- image
- signal
- module
- value
- region
- 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.)
- Pending
Links
- 230000007246 mechanism Effects 0.000 title claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 35
- 238000001914 filtration Methods 0.000 claims description 24
- 238000000034 method Methods 0.000 claims description 23
- 230000009466 transformation Effects 0.000 claims description 13
- 238000011156 evaluation Methods 0.000 claims description 10
- 230000003044 adaptive effect Effects 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000006243 chemical reaction Methods 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 7
- 238000013507 mapping Methods 0.000 claims description 7
- 239000011148 porous material Substances 0.000 claims description 7
- 238000003708 edge detection Methods 0.000 claims description 6
- 230000007704 transition Effects 0.000 claims description 6
- 230000000007 visual effect Effects 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 3
- 238000009415 formwork Methods 0.000 claims description 3
- 238000003672 processing method Methods 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 3
- 238000011946 reduction process Methods 0.000 claims description 3
- 239000004744 fabric Substances 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 3
- 230000007547 defect Effects 0.000 abstract description 2
- 230000008901 benefit Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000035800 maturation Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
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
- G06T7/155—Segmentation; Edge detection involving morphological operators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
- G06T7/41—Analysis of texture based on statistical description of texture
- G06T7/44—Analysis of texture based on statistical description of texture using image operators, e.g. filters, edge density metrics or local histograms
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- 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
- G06T2207/20036—Morphological image processing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Probability & Statistics with Applications (AREA)
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
The invention belongs to mechanical equipment technical fields, disclose a kind of tensioning integrated wheel type train of mechanism, are provided with the image collection module for obtaining snowfield, mountainous region, the various complicated ground images in marsh；The image processing module handled for the snowfield of the acquisition to image collection module, mountainous region, the various complicated ground images in marsh；For the control module of traveling speed command to be compared and issued to the image that image processing module is handled；Receive the instruction of control module, and the drive module executed；The display module shown for the instruction to control module.The present invention by it is mechanical to its from driving effect can the perfect requirement for adapting to such as snowfield, mountainous region, the various complicated grounds in marsh, at a high speed or low speed can stablize traveling, overcome traditional rubber wheel and blow out and the overweight defect easily ground of Athey wheel.
Description
Technical field
The invention belongs to mechanical equipment technical field more particularly to a kind of tensioning integrated wheel type trains of mechanism.
Background technique
Currently, the prior art commonly used in the trade is such thatWalking mechanism is set as the sports types machinery such as vehicle, robot
Standby important composition plays huge economic and social benefit in fields such as aerospace, meteorology, communication, military affairs.All kinds of
In walking mechanism, common wheel type traveling mechanism technology maturation, bearing capacity is strong, high-efficient；But energy is adapted in complicated ground environment
Power is poor, and easily blows out；Crawler type walking mechanism can advance freely on various ground, but hard ground speed is unhappy,
Maintenance is got up also more troublesome；And crawler type, mostly than cumbersome, component is easy to wear.
It is input to the process that output recognition result is a complicated image procossing from image, generally by three part groups
At: detection, Character segmentation and character recognition.Method is varied at present, can generally speaking be divided into two major classes: being based on cromogram
As the detection algorithm of processing technique and the detection algorithm based on gray level image processing technique.
It is mainly analyzed using intrinsic colour match based on color image processing: based on fuzzy training and color
Method of the edge to analysis；It is analyzed using hsv color model, tonal gradation classification is carried out to the image of input, in conjunction with
Mathematical morphology, word frequency statistic method judge position etc..
Time caused by gray level image processing technique is early compared with color image processing, the calculation amount of such algorithm
Smaller, arithmetic speed is very fast, and most algorithms are the bases using the bianry image after Binary Sketch of Grey Scale Image as algorithm.Than
Such as: maximum between-cluster variance method (otsu method), the method for local binarization, the binarization method based on texture information, Niblack
Binarization methods and Binarization methods (wavelet decomposition, unity and coherence in writing feature, projective clustering etc.) based on various features.
In conclusion the technical issues of existing walking mechanism, is:
Ride comfort, control stability, higher speed, low impact, light weight can not be combined in complex road condition downward driving
Change and durability.
That there are verification and measurement ratios is not high for present image acquiring method, is influenced by extraneous factor, robustness it is not strong etc. it is many because
Element.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of tensioning integrated wheel type trains of mechanism.
The invention is realized in this way a kind of tensioning integrated wheel type train of mechanism, the tensioning integrated wheel type train of mechanism
Include:
For obtaining the image collection module of snowfield, mountainous region, the various complicated ground images in marsh；
It is connect with image collection module, it is various intricately for the snowfield of the acquisition to image collection module, mountainous region, marsh
The image processing module that face picture is handled；
For the control module of traveling speed command to be compared and issued to the image that image processing module is handled；
Receive the instruction of control module, and the drive module executed；
The display module shown for the instruction to control module.
Further, the acquisition methods of described image acquisition module include:
Extract color characteristic and adaptive LBP operator feature；
Establish multiple features bottom order matrix table representation model；
It exports pseudo- region and obtains and determine precise area to the end；
Image gray processing, for convenience of the edge extracting of image, R, G, B using RGB image in Digital Image Processing are each
Color image is converted gray level image by the pixel value in channel and the transformational relation of gray level image pixel value, and formula is as follows:
Gray=R*0.3+G*0.59+B*0.11；
Edge extraction, using the Roberts operator edge detection technical role in digital image processing method in ash
The edge that image obtains image is spent, different detective operators have different edge detection templates, hand over according to specific formwork calculation
The difference of fork pixel is as follows using template as current pixel value:
E (i, j)=| F (i, j)-F (i+1, j+1) |+| F (i+1, j)-F (i, j+1) |；
Image procossing is filtered gray level image using high pass/low pass filter to construct image to be evaluated
Reference picture is traversed each pixel of image using Filtering Template, is placed in template center works as every time using 3*3 mean filter
Preceding pixel, the average value of all pixels is newly worth as current pixel using in template, and template is as follows:
Image border statistical information calculates, and respective edge grayscale information before and after image filtering is calculated separately, before filtering processing
Image F statistical information to be evaluated be sum_orig, reference picture F2 statistical information after filtering processing is sum_filter, tool
Body calculation formula is as follows:
Wherein, w1 and w2 is according to the weight set with a distance from center pixel, w1=1, w2=1/3；
Image blur index calculates, using the ratio of the image filtering front and rear edges grey-level statistics obtained as fuzzy
Index is spent, for convenience of evaluating, taking biggish is denominator, and lesser is molecule, keeps the value between (0,1)；
A corresponding fuzziness indication range [min, max] is obtained according to the DMOS range of the best visual effect；
Image blur adjustment illustrates to change very big, original image before and after image filtering if image blur index is less than min
As excessively sharpening, then adjustment is filtered using low-pass filter；If more than max, illustrate to vary less before and after image filtering, it is former
Image is excessively fuzzy, then is filtered adjustment using high-pass filter, reaches more preferably visual effect；
Step 9 obtains final image and the image blur evaluation index,
It exports pseudo- region and obtains and determine precise area to the end；And display module shows screen.
Further, extracting adaptive LBP operator characteristics algorithm includes:
(1) image of input system is converted into gray level image, summed to image { grayv (i, j) } grey scale pixel value, then
Obtain average value:
(2) background is removed using total textural characteristics, calculates the grey scale pixel value of image and the difference of mean pixel gray value
The sum of absolute value of value is averaged:
Background is removed using Local textural feature, with the sliding window of 3 × 3 sizes, traverses image, seeks center pixel ash
The difference of angle value and neighboring pixel gray value, the averaged in each video in window:
(3) method of the Fitting Calculation adaptive threshold:
Further, the multiple features bottom order matrix table representation model:
s.t.Xi=XiAi+Ei, i=1, L, K
Wherein α is greater than 0 coefficient,For measuring noise and wild point bring error；
The pseudo- region of the output simultaneously obtains determining precise area to the end；Specifically:
(1) according to image size, the ratio of acquisition, the external matrix of every sub-spaces is left as pseudo- region；
(2) a hopping function f (i, j) is set, pseudo- region is accurately positioned, determines the upper following of precise area
Boundary:
Wherein c (i, j) is
C (i, j)=LBP8,1(i, j)-LBP8,1(i, j-1)
I=1 in upper two formula, 2,3,4, Λ N, j=2,3,4, Λ M, therefore the transition times and S (i) of any a line i are as follows:
If any a line transition times and S (i >=12), this line just belong to precise area；From top to bottom to whole
Width image is scanned, and finds out all rows for meeting (i >=12) S, and records the line number i of this line；If there is continuous h row
Meet S (i >=12), then it is highly the rectangular area of h that obtaining a width, which is M, this region is exactly precise area, in image
Region without this feature excludes.
Further, the analysis method of the control module includes:
Inverse Fourier signal is converted and handled to input signal using built-in fast Fourier inverse transformation module；
String is turned and signal carries out constellation mapping processing first, then serial conversion is parallel signal；
Built-in precoding unit output signal is handled；
Signal amplitude is estimated；
Circulation prefix processing is first added to the signal of parallel-serial conversion, parallel signal is being converted to serial signal.
Further, the fast Fourier inverse transformation module further comprises:
Butterfly arithmetic element, for passing through coefficientSymmetry, periodicity and reducibility abbreviation, find out 4 N/4 points
DFT, i.e. X1(K)、X2(K)、X3(K) and X4(K), whole X is just found out(K)Value；
Filter is connect with the butterfly arithmetic element, is generated inside and outside out-of-band frequency and system by transformation for filtering out
Noise；
Twiddle factor unit is connect with the butterfly arithmetic element, should for storing complex constant needed for butterfly computation
Constant is located on the unit circle of complex number plane；
Ping-pang cache structure is connect with the filter, for solving burst reception high data rate and reduction process rate
Between contradiction, realize generating date；
It is described to further comprise to signal amplitude estimation:
Obtain each sample point data of signal；
Calculate the signal amplitude of each sampled point signal；
Described handled again parallel-serial conversion output signal further comprises:
The threshold value of each modulation carrier wave is judged by look-up table；
The gain coefficient of each carrier wave is calculated according to threshold value；
It is described to further comprise to input signal transformation and the inverse Fourier signal of processing:
Realize the mapping of star-like figure, the leggy modulation of signal；
Reduction rate is carried out to the signal of input；
The delay of signal prejudges；
Raising rate is carried out to input signal.
Further, the drive module includes connector, support rod and spring, and the connector is hollow cylindrical structure,
And the marginal position of cylindrical central is provided with ten connecting pores, hole is evenly distributed；
Support rod is provided with 20, is divided into I type bar and II type bar；Wherein I type bar and each ten, II type bar, an I type
Bar is combined with an II type bar in X-type, is connect respectively with two adjacent connecting pores；Amount to ten groups of X-type connecting rods.
Connecting rod two sides described in every group are fixed with a spring, outside is fixed with spring.
Each bar I, bar II, both ends are semicircular arc, and middle section bar I is Filled Rectangle, and bar II is hollow
Rectangle.
Advantages of the present invention and good effect are as follows:
The tensioning integrated wheel type train of mechanism perfect can adapt to such as snowfield, mountain from driving effect to it by mechanical
The requirement of the various complicated grounds such as ground, marsh, high speed or low speed can stablize traveling, overcome traditional rubber wheel and blow out and crawler belt
The overweight defect easily ground is taken turns, especially when machinery is completely superior to traditional type mechanism in the complex condition mechanism；The present invention is led
First wheel innovation research is carried out using tension integral structure；Application of the tension integral structure in field of reality is also promoted simultaneously.
The present invention combines improved LRR model and morphological operation to obtain image precise area；Image inspection can be effectively improved
The robustness and accuracy of survey reduce erroneous detection.
Texture analysis can be carried out to the image under complex background, more accurate characteristic information can be provided.
Image blur evaluation method provided by the invention, different from traditional evaluation method, the present invention is from relative evaluation
Angle set out, construct the reference picture of image to be evaluated using filter, calculate variation front and back image border statistical information
Ratio is as evaluation index.The principle of the present invention is simple, realizes the content independence and real-time of image blur evaluation, can
With the fuzziness between any image of quick and precisely evaluation comparison.
In the analysis method of control module provided by the invention, fast Fourier inverse transformation unit further comprises butterfly fortune
Unit, filter, twiddle factor unit and ping-pang cache structure are calculated, multicarrier mapping is realized；Butterfly arithmetic element uses 4 pairs
RAM*2 stores the operand of butterfly operation, so that arithmetic speed greatly improved；Twiddle factor unit use look-up table, also plus
The fast execution speed of algorithm；Ping-pang cache structure is configured to ping-pong structure, further improves arithmetic speed；Precoding list
Member uses excellent Zadoff-Chu sequences algorithm, has good autocorrelation and cross correlation, advantageously reduces
Intersymbol interference, while Zadoff-Chu sequence has symmetry, can reduce the complexity of sequence generation.
Compared with prior art, technical advantage of the invention is as follows:
The present invention greatly simplifies Zadoff-Chu sequences algorithm, greatly reduces the complexity of program realization
Degree.
The present invention has lower peak than single Zadoff-Chu sequences precoding method and single companding method
Compare.
The present invention is made of extensive field programmable device, can be realized by configuring different programs to work
The flexible modification of parameter, device structure are simplified, and cost significantly reduces.
Detailed description of the invention
Fig. 1 is tensioning entirety walking mechanism system schematic provided in an embodiment of the present invention；
Fig. 2 is tensioning entirety walking mechanism overall system architecture schematic diagram provided in an embodiment of the present invention；
In figure: 101, connector；201, support rod I；202, support rod II；301, spring I；401, spring II；501, it is coupled
Hole.
Fig. 3 is tensioning entirety walking mechanism system block diagram provided in an embodiment of the present invention.
In figure: 6, image collection module；7, image processing module；8, control module；9, drive module；10, display module.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
With reference to the accompanying drawing and specific embodiment is further described application principle of the invention.
It includes connector 101, support rod 201,202, spring 301,401 that Fig. 1, which is drive module provided in an embodiment of the present invention,
The connector 101 is hollow cylindrical structure, and the marginal position of cylindrical central is provided with ten connecting pores.
Support rod is provided with 20, I type bar 201 and 202 each ten, II type bar, and an I type bar and an II type bar are in X
Type combination, connect with adjacent connector connecting pore 501 respectively；Amount to ten groups of X-type connecting rods；One bullet in every group of connecting rod two sides
Spring 301 is fixed, outside is fixed with spring 401；Every group of connecting rod two sides use a spring 301 to fix, outside spring 401 is solid
It is fixed；Each bar is hinged with the rotation of the special-shaped bar of adjacent sets, and each connecting pore is cut with scissors with a bar I201 and a bar II202 respectively
It connects, with the connection of spring 401 on the outside of two adjacent groups.
Each bar I201, bar II202, both ends are semicircular arc, and middle section bar I201 is Filled Rectangle, bar
II202 is hollow rectangle.
The drive module further includes driving motor etc., is connect with control module, and the driving motor passes through drive shaft
It is connect with drive sub, driving motor drives support rod I, support rod II, spring I, spring II corresponding sports.
The structure can be used as the mechanical support walking mechanism of landform work complicated and changeable, lead in exploration, rescue, military affairs etc.
Domain has broad application prospects.
Below with reference to concrete analysis, the invention will be further described.
Such as Fig. 3, tensioning integrated wheel type train of mechanism provided in an embodiment of the present invention, comprising:
For obtaining the image collection module 6 of snowfield, mountainous region, the various complicated ground images in marsh；
It is connect with image collection module, it is various intricately for the snowfield of the acquisition to image collection module, mountainous region, marsh
The image processing module 7 that face picture is handled；
For the control module of traveling speed command to be compared and issued to the image that image processing module is handled
8；
Receive the instruction of control module, and the drive module 9 executed；
The display module 10 shown for the instruction to control module.
Described image obtain module acquisition methods include:
Extract color characteristic and adaptive LBP operator feature；
Establish multiple features bottom order matrix table representation model；
It exports pseudo- region and obtains and determine precise area to the end；
Image gray processing, for convenience of the edge extracting of image, R, G, B using RGB image in Digital Image Processing are each
Color image is converted gray level image by the pixel value in channel and the transformational relation of gray level image pixel value, and formula is as follows:
Gray=R*0.3+G*0.59+B*0.11；
Edge extraction, using the Roberts operator edge detection technical role in digital image processing method in ash
The edge that image obtains image is spent, different detective operators have different edge detection templates, hand over according to specific formwork calculation
The difference of fork pixel is as follows using template as current pixel value:
E (i, j)=| F (i, j)-F (i+1, j+1) |+| F (i+1, j)-F (i, j+1) |；
Image procossing is filtered gray level image using high pass/low pass filter to construct image to be evaluated
Reference picture is traversed each pixel of image using Filtering Template, is placed in template center works as every time using 3*3 mean filter
Preceding pixel, the average value of all pixels is newly worth as current pixel using in template, and template is as follows:
Image border statistical information calculates, and respective edge grayscale information before and after image filtering is calculated separately, before filtering processing
Image F statistical information to be evaluated be sum_orig, reference picture F2 statistical information after filtering processing is sum_filter, tool
Body calculation formula is as follows:
Wherein, w1 and w2 is according to the weight set with a distance from center pixel, w1=1, w2=1/3；
Image blur index calculates, using the ratio of the image filtering front and rear edges grey-level statistics obtained as fuzzy
Index is spent, for convenience of evaluating, taking biggish is denominator, and lesser is molecule, keeps the value between (0,1)；
A corresponding fuzziness indication range [min, max] is obtained according to the DMOS range of the best visual effect；
Image blur adjustment illustrates to change very big, original image before and after image filtering if image blur index is less than min
As excessively sharpening, then adjustment is filtered using low-pass filter；If more than max, illustrate to vary less before and after image filtering, it is former
Image is excessively fuzzy, then is filtered adjustment using high-pass filter, reaches more preferably visual effect；
Step 9 obtains final image and the image blur evaluation index,
It exports pseudo- region and obtains and determine precise area to the end；And display module shows screen.
Extracting adaptive LBP operator characteristics algorithm includes:
(1) image of input system is converted into gray level image, summed to image { grayv (i, j) } grey scale pixel value, then
Obtain average value:
(2) background is removed using total textural characteristics, calculates the grey scale pixel value of image and the difference of mean pixel gray value
The sum of absolute value of value is averaged:
Background is removed using Local textural feature, with the sliding window of 3 × 3 sizes, traverses image, seeks center pixel ash
The difference of angle value and neighboring pixel gray value, the averaged in each video in window:
(3) method of the Fitting Calculation adaptive threshold:
The multiple features bottom order matrix table representation model:
s.t.Xi=XiAi+Ei, i=1, L, K
Wherein α is greater than 0 coefficient,For measuring noise and wild point bring error；
The pseudo- region of the output simultaneously obtains determining precise area to the end；Specifically:
(1) according to image size, the ratio of acquisition, the external matrix of every sub-spaces is left as pseudo- region；
(2) a hopping function f (i, j) is set, pseudo- region is accurately positioned, determines the upper following of precise area
Boundary:
Wherein c (i, j) is
C (i, j)=LBP8,1(i, j)-LBP8,1(i, j-1)
I=1 in upper two formula, 2,3,4, Λ N, j=2,3,4, Λ M, therefore the transition times and S (i) of any a line i
Are as follows:
If any a line transition times and S (i >=12), this line just belong to precise area；From top to bottom to whole
Width image is scanned, and finds out all rows for meeting (i >=12) S, and records the line number i of this line；If there is continuous h row
Meet S (i >=12), then it is highly the rectangular area of h that obtaining a width, which is M, this region is exactly precise area, in image
Region without this feature excludes.
The analysis method of the control module includes:
Inverse Fourier signal is converted and handled to input signal using built-in fast Fourier inverse transformation module；
String is turned and signal carries out constellation mapping processing first, then serial conversion is parallel signal；
Built-in precoding unit output signal is handled；
Signal amplitude is estimated；
Circulation prefix processing is first added to the signal of parallel-serial conversion, parallel signal is being converted to serial signal.
The fast Fourier inverse transformation module further comprises:
Butterfly arithmetic element, for passing through coefficientSymmetry, periodicity and reducibility abbreviation, find out 4 N/4 points
DFT, i.e. X1(K)、X2(K)、X3(K) and X4(K), whole X is just found out(K)Value；
Filter is connect with the butterfly arithmetic element, is generated inside and outside out-of-band frequency and system by transformation for filtering out
Noise；
Twiddle factor unit is connect with the butterfly arithmetic element, should for storing complex constant needed for butterfly computation
Constant is located on the unit circle of complex number plane；
Ping-pang cache structure is connect with the filter, for solving burst reception high data rate and reduction process rate
Between contradiction, realize generating date；
It is described to further comprise to signal amplitude estimation:
Obtain each sample point data of signal；
Calculate the signal amplitude of each sampled point signal；
Described handled again parallel-serial conversion output signal further comprises:
The threshold value of each modulation carrier wave is judged by look-up table；
The gain coefficient of each carrier wave is calculated according to threshold value；
It is described to further comprise to input signal transformation and the inverse Fourier signal of processing:
Realize the mapping of star-like figure, the leggy modulation of signal；
Reduction rate is carried out to the signal of input；
The delay of signal prejudges；
Raising rate is carried out to input signal.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (7)
1. a kind of tensioning integrated wheel type train of mechanism, which is characterized in that the tensioning integrated wheel type train of mechanism includes:
For obtaining the image collection module of snowfield, mountainous region, the various complicated ground images in marsh；
It is connect with image collection module, for the snowfield of the acquisition to image collection module, mountainous region, the various intricately faces in marsh
As the image processing module handled；
For the control module of traveling speed command to be compared and issued to the image that image processing module is handled；
Receive the instruction of control module, and the drive module executed；
The display module shown for the instruction to control module.
2. tensioning integrated wheel type train of mechanism as described in claim 1, which is characterized in that the acquisition of described image acquisition module
Method includes:
Extract color characteristic and adaptive LBP operator feature；
Establish multiple features bottom order matrix table representation model；
It exports pseudo- region and obtains and determine precise area to the end；
Image gray processing utilizes each channel R, G, B of RGB image in Digital Image Processing for convenience of the edge extracting of image
Pixel value and the transformational relation of gray level image pixel value convert gray level image for color image, formula is as follows:
Gray=R*0.3+G*0.59+B*0.11；
Edge extraction, using the Roberts operator edge detection technical role in digital image processing method in grayscale image
Edge as obtaining image, different detective operators have different edge detection templates, according to specific formwork calculation chiasmal image
The difference of element is as follows using template as current pixel value:
E (i, j)=| F (i, j)-F (i+1, j+1) |+| F (i+1, j)-F (i, j+1) |；
Image procossing is filtered gray level image using high pass/low pass filter to construct the reference of image to be evaluated
Image is traversed each pixel of image using Filtering Template, template center is placed in current picture every time using 3*3 mean filter
Element, the average value of all pixels is newly worth as current pixel using in template, and template is as follows:
Image border statistical information calculates, and calculates separately before and after image filtering respectively edge grayscale information, before filtering processing to
Evaluation image F statistical information is sum_orig, and the reference picture F2 statistical information after filtering processing is sum_filter, specific to count
It is as follows to calculate formula:
Wherein, w1 and w2 is according to the weight set with a distance from center pixel, w1=1, w2=1/3；
Image blur index calculates, and the ratio of the image filtering front and rear edges grey-level statistics obtained is referred to as fuzziness
Mark, for convenience of evaluating, taking biggish is denominator, and lesser is molecule, keeps the value between (0,1)；
A corresponding fuzziness indication range [min, max] is obtained according to the DMOS range of the best visual effect；
Image blur adjustment illustrates to change very big, original image mistake before and after image filtering if image blur index is less than min
In sharpening, then adjustment is filtered using low-pass filter；If more than max, illustrate to vary less before and after image filtering, original image
It is excessively fuzzy, then it is filtered adjustment using high-pass filter, reaches more preferably visual effect；
Step 9 obtains final image and the image blur evaluation index,
It exports pseudo- region and obtains and determine precise area to the end；And display module shows screen.
3. tensioning integrated wheel type train of mechanism as claimed in claim 2, which is characterized in that extract adaptive LBP operator feature
Algorithm includes:
(1) image of input system is converted into gray level image, summed to image { grayv (i, j) } grey scale pixel value, then obtain
Average value:
(2) background is removed using total textural characteristics, calculates the grey scale pixel value of image and the difference of mean pixel gray value
The sum of absolute value is averaged:
Background is removed using Local textural feature, with the sliding window of 3 × 3 sizes, image is traversed, seeks center pixel gray value
And the difference of neighboring pixel gray value, the averaged in each video in window:
(3) method of the Fitting Calculation adaptive threshold:
4. tensioning integrated wheel type train of mechanism as claimed in claim 2, which is characterized in that the multiple features bottom order matrix indicates
Model:
s.t.Xi=XiAi+Ei, i=1, L, K
Wherein d is greater than 0 coefficient,For measuring noise and wild point bring error；
The pseudo- region of the output simultaneously obtains determining precise area to the end；Specifically:
(1) according to image size, the ratio of acquisition, the external matrix of every sub-spaces is left as pseudo- region；
(2) a hopping function f (i, j) is set, pseudo- region is accurately positioned, determines the up-and-down boundary of precise area:
Wherein c (i, j) is
C (i, j)=LBP8,1(i, j)-LBP8,1(i, j-1)
I=1 in upper two formula, 2,3,4, ∧ N, j=2,3,4, ∧ M, therefore the transition times and S (i) of any a line i are as follows:
If any a line transition times and S (i >=12), this line just belong to precise area；From top to bottom to whole picture figure
As being scanned, all rows for meeting (i >=12) S are found out, and record the line number i of this line；If there is continuous h row meets
S (i >=12), then it is highly the rectangular area of h, this region is exactly precise area, is not had in image that obtaining a width, which is M,
There is the region of this feature to exclude.
5. tensioning integrated wheel type train of mechanism as described in claim 1, which is characterized in that the analysis method of the control module
Include:
Inverse Fourier signal is converted and handled to input signal using built-in fast Fourier inverse transformation module；
String is turned and signal carries out constellation mapping processing first, then serial conversion is parallel signal；
Built-in precoding unit output signal is handled；
Signal amplitude is estimated；
Circulation prefix processing is first added to the signal of parallel-serial conversion, parallel signal is being converted to serial signal.
6. tensioning integrated wheel type train of mechanism as claimed in claim 5, which is characterized in that the fast Fourier inverse transformation mould
Block further comprises:
Butterfly arithmetic element, for passing through coefficientSymmetry, periodicity and reducibility abbreviation, find out the DFT of 4 N/4 points,
That is X1(K)、X2(K)、X3(K) and X4(K), whole X is just found out(K)Value；
Filter is connect with the butterfly arithmetic element, for filtering out making an uproar of generating inside and outside out-of-band frequency and system by transformation
Sound；
Twiddle factor unit is connect with the butterfly arithmetic element, for storing complex constant needed for butterfly computation, the constant
On the unit circle of complex number plane；
Ping-pang cache structure is connect with the filter, for solving between burst reception high data rate and reduction process rate
Contradiction, realize generating date；
It is described to further comprise to signal amplitude estimation:
Obtain each sample point data of signal；
Calculate the signal amplitude of each sampled point signal；
Described handled again parallel-serial conversion output signal further comprises:
The threshold value of each modulation carrier wave is judged by look-up table；
The gain coefficient of each carrier wave is calculated according to threshold value；
It is described to further comprise to input signal transformation and the inverse Fourier signal of processing:
Realize the mapping of star-like figure, the leggy modulation of signal；
Reduction rate is carried out to the signal of input；
The delay of signal prejudges；
Raising rate is carried out to input signal.
7. tensioning integrated wheel type train of mechanism as described in claim 1, the drive module includes connector, support rod and bullet
Spring, the connector is hollow cylindrical structure, and the marginal position of cylindrical central is provided with ten connecting pores, and hole is in uniform point
Cloth；
Support rod is provided with 20, is divided into I type bar and II type bar；Wherein I type bar and each ten, II type bar, I type bar with
One II type bar is combined in X-type, is connect respectively with two adjacent connecting pores；Amount to ten groups of X-type connecting rods；
Connecting rod two sides described in every group are fixed with a spring, outside is coupled with spring；
Each bar I, bar II, both ends are semicircular arc, and middle section bar I is Filled Rectangle, and bar II is hollow rectangle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810275205.9A CN110163883A (en) | 2018-03-29 | 2018-03-29 | A kind of tensioning integrated wheel type train of mechanism |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810275205.9A CN110163883A (en) | 2018-03-29 | 2018-03-29 | A kind of tensioning integrated wheel type train of mechanism |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110163883A true CN110163883A (en) | 2019-08-23 |
Family
ID=67636420
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810275205.9A Pending CN110163883A (en) | 2018-03-29 | 2018-03-29 | A kind of tensioning integrated wheel type train of mechanism |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110163883A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101269678A (en) * | 2008-05-16 | 2008-09-24 | 武汉理工大学 | Wheeled robot with traveling system |
| CN103810474A (en) * | 2014-02-14 | 2014-05-21 | 西安电子科技大学 | Car plate detection method based on multiple feature and low rank matrix representation |
| CN104200480A (en) * | 2014-09-17 | 2014-12-10 | 西安电子科技大学宁波信息技术研究院 | Image fuzzy degree evaluation method and system applied to intelligent terminal |
| CN105069411A (en) * | 2015-07-24 | 2015-11-18 | 深圳市佳信捷技术股份有限公司 | Road recognition method and device |
| CN105306403A (en) * | 2015-10-28 | 2016-02-03 | 西安邮电大学 | Method and system for reducing peak-to-average ratio of transmitting signal of OFDM system |
-
2018
- 2018-03-29 CN CN201810275205.9A patent/CN110163883A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101269678A (en) * | 2008-05-16 | 2008-09-24 | 武汉理工大学 | Wheeled robot with traveling system |
| CN103810474A (en) * | 2014-02-14 | 2014-05-21 | 西安电子科技大学 | Car plate detection method based on multiple feature and low rank matrix representation |
| CN104200480A (en) * | 2014-09-17 | 2014-12-10 | 西安电子科技大学宁波信息技术研究院 | Image fuzzy degree evaluation method and system applied to intelligent terminal |
| CN105069411A (en) * | 2015-07-24 | 2015-11-18 | 深圳市佳信捷技术股份有限公司 | Road recognition method and device |
| CN105306403A (en) * | 2015-10-28 | 2016-02-03 | 西安邮电大学 | Method and system for reducing peak-to-average ratio of transmitting signal of OFDM system |
Non-Patent Citations (1)
| Title |
|---|
| 季聪: "智能车路径识别与控制研究", 《电视技术》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104408707B (en) | Rapid digital imaging fuzzy identification and restored image quality assessment method | |
| CN116758059B (en) | Visual nondestructive testing method for roadbed and pavement | |
| Mitianoudis et al. | Document image binarization using local features and Gaussian mixture modeling | |
| CN108416348A (en) | Plate location recognition method based on support vector machines and convolutional neural networks | |
| CN106446952A (en) | Method and apparatus for recognizing score image | |
| CN103871062B (en) | A kind of lunar surface rock detection method described based on super-pixel | |
| CN111080602B (en) | Method for detecting foreign matters in water leakage hole of railway wagon | |
| CN104102928B (en) | A kind of Classifying Method in Remote Sensing Image based on texture primitive | |
| CN108122221A (en) | The dividing method and device of diffusion-weighted imaging image midbrain ischemic area | |
| CN108921076B (en) | Pavement crack disease self-adaptive constant false alarm detection method based on image | |
| CN112069985B (en) | High-resolution field image rice spike detection and counting method based on deep learning | |
| CN109871875A (en) | A kind of building change detecting method based on deep learning | |
| CN111145163A (en) | Paper wrinkle defect detection method and device | |
| CN103164852A (en) | Image processing device and image processing method | |
| CN111079734B (en) | Method for detecting foreign matters in triangular holes of railway wagon | |
| CN110677639A (en) | Non-reference video quality evaluation method based on feature fusion and recurrent neural network | |
| CN114897898A (en) | Wood board quality classification method based on image processing | |
| CN104992429A (en) | Mountain crack detection method based on image local reinforcement | |
| CN107016680B (en) | A kind of pest image background minimizing technology based on conspicuousness detection | |
| CN110428450A (en) | Dimension self-adaption method for tracking target applied to the mobile inspection image of mine laneway | |
| CN113888536B (en) | Printed matter double image detection method and system based on computer vision | |
| CN108830856A (en) | A kind of GA automatic division method based on time series SD-OCT retinal images | |
| CN109708658A (en) | A kind of visual odometry method based on convolutional neural networks | |
| CN116630813A (en) | Highway road surface construction quality intelligent detection system | |
| CN117094914A (en) | Smart city road monitoring system based on computer vision |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190823 |