CN108573121A - A kind of non-linear Architectural Design method of adjustment and system - Google Patents
A kind of non-linear Architectural Design method of adjustment and system Download PDFInfo
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Abstract
The invention belongs to technical field of buildings, discloses a kind of non-linear Architectural Design method of adjustment and system, the non-linear Architectural Design adjustment system include：Environmental factor acquisition module, main control module, nonlinear images make module, nonlinear images enhancing module, building shape generation module, VR and module are presented.The present invention enhances module by nonlinear images and effectively eliminates picture noise and enhancing image effect, overcome the limitation of linear method, with stronger practicability, and edge extracting is carried out after image enhancement, the Boundary extracting algorithm of use has preferably detection property, polarization and minimum response, and image outline is made more clearly to be extracted；Module is presented by VR simultaneously can carry out virtual reality experiencing architecture on the spot in person after producing building shape, more convenient to carry out parameters adjustment to building.
Description
Technical field
The invention belongs to technical field of buildings more particularly to a kind of non-linear Architectural Design methods of adjustment and system.
Background technology
Non-linear building is a kind of body of continuous flowing shape, and this body came from as a result to building performance and week
The analysis of surrounding environment factor.The design process of building is the researchs on various influence architectural factors, and by refining and integrating,
By various impact factors from concept development to image, the final body as building.The non-linear body of shape is flowed not only in shape
Computer software technology is depended in the generation of body, and is fixed against Computer-aided manufacturing in the construction of body
(CAM).However, nonlinear images blur margin is clear during existing non-linear Architectural Design, effect is bad；It has designed simultaneously
Observing effect on computer can only be confined to after, the building shape of impression design that cannot be on the spot in person is unfavorable for constructing
Stage is more effectively adjusted.
In conclusion problem of the existing technology is：Nonlinear images side during existing non-linear Architectural Design
Edge is unintelligible, and effect is bad；It can only be confined to observing effect on computer after the completion of designing simultaneously, impression that cannot be on the spot in person
The building shape of design is unfavorable for more effectively being adjusted in the construction stage.
Invention content
In view of the problems of the existing technology, the present invention provides a kind of non-linear Architectural Design method of adjustment and systems.
The invention is realized in this way a kind of non-linear Architectural Design adjustment system includes：
Environmental factor acquisition module, main control module, nonlinear images make module, nonlinear images enhancing module, building
Module is presented in form-creation module, VR；
Environmental factor acquisition module, connect with main control module, landform, temperature, humidity for acquiring environment etc. influence because
Element；
Main control module makes module with environmental factor acquisition module, nonlinear images, nonlinear images enhancing module, builds
Form-creation module, VR presentation module connections are built, the data information for environmental factor acquisition module to be transmitted to is deposited
Storage is analyzed with processing；
Nonlinear images make module, are connect with main control module, the data for the acquisition of combining environmental factor acquisition module
Nonlinear images are produced by computer modeling technique；
The nonlinear images make the iterative model of the data for projection calculating target image of module, the formula of iterative model
It is expressed as：
Wherein, X is the target image, and M is sytem matrix, and G is the data for projection, and i indicates iterations, XiIt indicates
The iteration result obtained after ith iteration；λ indicates convergence coefficient, and λ ∈ (0,1), M T indicate the transposition to matrix M；Setting
The initial value of the target image, and utilize the iterative model in the target image according to pre-set iterations
Each pixel be iterated update, obtain the target image, the current grayvalue of the pixel in the iterative model
With the gray value Uniform approximat of previous iteration；The pixel zero setting by gray value in target image less than 0；
Nonlinear images enhance module, are connect with main control module, for eliminating picture noise and being carried to image border
Take the image that acquisition is more clear；
Building shape generation module, connect with main control module, and building shape model is established for passing through nonlinear images；
Module is presented in VR, is connect with main control module, for building shape modelling at vr videos and to be passed through vr eyes
Realize the scene of virtual image；
The prior information extraction that module image super-resolution rebuilding is presented in the VR is as follows：
(1) it is that training sample set chooses N group image fritter training samples pair in sinusoidal area imagePer group picture
As fritter training sample is to comprising a high-resolution sine area image fritter and low resolution sine area image fritter, being arranged word
Allusion quotation size is K；
(2) dictionary is initializedOuter circulation iterations n, interior loop iteration number t；
(3) for all N groups image fritter training samples pair, gradient is calculated
GradientIt can be calculated according to following formula；
WhereinTo choose image fritter in the low resolution sine domain sample set of input,For the high score of input
Image fritter is chosen in the sample set of resolution sine domain,For rarefaction representation, λ (0≤λ≤1) indicates that relaxation factor, N are sampling
Number, αiIt is indicated for the sparse coding of x：
L indicates quadratic term loss, by asking the minimum of above formula that can optimize Dx,Dy, as follows：
||Dx(:,k)||2≤ 1, | | Dy (:,k)||2≤ 1, k=1, L, K
S.t. indicate constrained in since above formula is not easy to solve, introducing regularization mutually solves, therefore above formula is variable
For：
Here γ (0 λ≤1 ＜) is for balancing the parameter between two formulas.
WhereinzjFor j-th of element of z, Ω indicates the collection of all situations of j
It closes；
(4) for all N groups image fritter training samples pair, update
(5) after all N groups image fritter training samples are to all having calculated, update
GradientIt can be calculated according to following formula；
WhereinTo choose image fritter in the low resolution sine domain sample set of input,For the high score of input
Image fritter is chosen in the sample set of resolution sine domain,For rarefaction representation, λ (0≤λ≤1) indicates that relaxation factor, N are sampling
Number, αiIt is indicated for the sparse coding of x：
L indicates quadratic term loss, by asking the minimum of above formula that can optimize Dx,Dy, as follows：
||Dx(:,k)||2≤ 1, | | Dy (:,k)||2≤ 1, k=1, L, K
S.t. indicate constrained in since above formula is not easy to solve, introducing regularization mutually solves, therefore above formula is variable
For：
Here γ (0 λ≤1 ＜) is for balancing the parameter between two formulas；
WhereinzjFor j-th of element of z, Ω indicates the collection of all situations of j
It closes；
(6) repeat step (3) to step (5) untilConvergence；
(7) output doubledictionary Dx, Dy。
A kind of non-linear Architectural Design method of adjustment includes the following steps：
Step 1 acquires the influence factors such as landform, temperature, the humidity of environment by environmental factor acquisition module；
The data information that environmental factor acquisition module is transmitted to is carried out storage and handled point by step 2, main control module
Analysis；
Step 3, the data that the acquisition of module combining environmental factor acquisition module is made by nonlinear images pass through computer
Analogue technique produces nonlinear images；
Step 4 enhances module elimination picture noise by nonlinear images and extracts acquisition more to image border
Clearly image；
Step 5 establishes building shape model, and module is presented by VR to build shape by building shape generation module
Body Model is fabricated to vr videos and realizes the scene of virtual image by vr eyes.
Further, the nonlinear images enhancing module Enhancement Method is as follows：
First, the coloured image of acquisition is converted into gray level image；
Then, the gray level image is filtered using median filter method：
G (x, y)=Med { f (x-k, y-l) }, (k, l ∈ W)；
Wherein, f (x, y) is original image, and g (x, y) is the image after medium filtering, and W is two dimension pattern plate, and Med is extraction
Median operation functional symbol；
Finally, image equilibration：
The frequency that k-th of gray level of image occurs before calculating equalization processing：
Ps(Sk)=nk/ n, l≤k≤N；
Wherein, nk is k-th of gray-level pixels number, and n is sum of all pixels, and N is gray level sum；
The gray value cumulative distribution function of gray level image before calculating equalization processing：
Pixel value in gray level image is adjusted, keeps the gray value cumulative distribution function of gray level image after equalization processing full
Foot：
Wherein, M is the sum of all pixels of gray level image.
Advantages of the present invention and good effect are：The present invention enhances module by nonlinear images and effectively eliminates picture noise
And enhancing image effect, the limitation of linear method is overcome, there is stronger practicability, and carry out edge after image enhancement
The Boundary extracting algorithm of extraction, use has preferably detection property, polarization and minimum response, and image outline is made to be become apparent from
Ground extracts；Module is presented by VR simultaneously can carry out virtual reality experience on the spot in person after producing building shape
Building, it is more convenient that parameters adjustment is carried out to building.
Description of the drawings
Fig. 1 is that the present invention implements the non-linear Architectural Design method of adjustment flow chart provided.
Fig. 2 is that the present invention implements the non-linear Architectural Design provided adjustment system structure diagram.
In Fig. 2：1, environmental factor acquisition module；2, main control module；3, nonlinear images make module；4, nonlinear images
Enhance module；5, building shape generation module；6, module is presented in VR.
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be 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.
Below in conjunction with the accompanying drawings and specific embodiment is further described the application principle of the present invention.
As shown in Figure 1, a kind of non-linear Architectural Design method of adjustment provided by the invention includes the following steps：
Step S101 acquires the influence factors such as landform, temperature, the humidity of environment by environmental factor acquisition module；
The data information that environmental factor acquisition module is transmitted to is carried out storage and handled point by step S102, main control module
Analysis；
Step S103, the data that the acquisition of module combining environmental factor acquisition module is made by nonlinear images pass through calculating
Machine analogue technique produces nonlinear images；
Step S104 enhances module elimination picture noise by nonlinear images and extracts acquisition more to image border
Add clearly image；
Step S105 establishes building shape model, and module is presented by VR to build by building shape generation module
The scene of virtual image is realized in body modelling at vr videos and by vr eyes.
As shown in Fig. 2, non-linear Architectural Design adjustment system provided by the invention includes：Environmental factor acquisition module 1, master
Control module 2, nonlinear images make module 3, module is presented in nonlinear images enhancing module 4, building shape generation module 5, VR
6。
Environmental factor acquisition module 1 is connect with main control module 2, the influences such as landform, temperature, humidity for acquiring environment
Factor；
Main control module 2 makes module 3 with environmental factor acquisition module 1, nonlinear images, nonlinear images enhance module
4, building shape generation module 5, VR are presented module 6 and connect, the data information for environmental factor acquisition module 1 to be transmitted to
It carries out storage and handles analysis；
Nonlinear images make module 3, are connect with main control module 2, the number for the acquisition of combining environmental factor acquisition module
Nonlinear images are produced according to by computer modeling technique；
Nonlinear images enhance module 4, are connect with main control module 2, for eliminating picture noise and being carried out to image border
Extraction obtains the image being more clear；
Building shape generation module 5 is connect with main control module 2, and building shape model is established for passing through nonlinear images；
Module 6 is presented in VR, is connect with main control module 2, for building shape modelling at vr videos and to be passed through vr
Eyeball realizes the scene of virtual image.
The nonlinear images make the iterative model of the data for projection calculating target image of module, the formula of iterative model
It is expressed as：
Wherein, X is the target image, and M is sytem matrix, and G is the data for projection, and i indicates iterations, XiIt indicates
The iteration result obtained after ith iteration；λ indicates convergence coefficient, and λ ∈ (0,1), M T indicate the transposition to matrix M；Setting
The initial value of the target image, and utilize the iterative model in the target image according to pre-set iterations
Each pixel be iterated update, obtain the target image, the current grayvalue of the pixel in the iterative model
With the gray value Uniform approximat of previous iteration；The pixel zero setting by gray value in target image less than 0；
The prior information extraction that module image super-resolution rebuilding is presented in the VR is as follows：
(1) it is that training sample set chooses N group image fritter training samples pair in sinusoidal area imagePer group picture
As fritter training sample is to comprising a high-resolution sine area image fritter and low resolution sine area image fritter, being arranged word
Allusion quotation size is K；
(2) dictionary is initializedOuter circulation iterations n, interior loop iteration number t；
(3) for all N groups image fritter training samples pair, gradient is calculated
GradientIt can be calculated according to following formula；
WhereinTo choose image fritter in the low resolution sine domain sample set of input,For the high score of input
Image fritter is chosen in the sample set of resolution sine domain,For rarefaction representation, λ (0≤λ≤1) indicates that relaxation factor, N are sampling
Number, αiIt is indicated for the sparse coding of x：
L indicates quadratic term loss, by asking the minimum of above formula that can optimize Dx,Dy, as follows：
||Dx(:,k)||2≤ 1, | | Dy (:,k)||2≤ 1, k=1, L, K
S.t. indicate constrained in since above formula is not easy to solve, introducing regularization mutually solves, therefore above formula is variable
For：
Here γ (0 λ≤1 ＜) is for balancing the parameter between two formulas.
WhereinzjFor j-th of element of z, Ω indicates the collection of all situations of j
It closes；
(4) for all N groups image fritter training samples pair, update
(5) after all N groups image fritter training samples are to all having calculated, update
GradientIt can be calculated according to following formula；
WhereinTo choose image fritter in the low resolution sine domain sample set of input,For the high score of input
Image fritter is chosen in the sample set of resolution sine domain,For rarefaction representation, λ (0≤λ≤1) indicates that relaxation factor, N are sampling
Number, αiIt is indicated for the sparse coding of x：
L indicates quadratic term loss, by asking the minimum of above formula that can optimize Dx,Dy, as follows：
||Dx(:,k)||2≤ 1, | | Dy (:,k)||2≤ 1, k=1, L, K
S.t. indicate constrained in since above formula is not easy to solve, introducing regularization mutually solves, therefore above formula is variable
For：
Here γ (0 λ≤1 ＜) is for balancing the parameter between two formulas；
WhereinzjFor j-th of element of z, Ω indicates the collection of all situations of j
It closes；
(6) repeat step (3) to step (5) untilConvergence；
(7) output doubledictionary Dx, Dy。
Nonlinear images enhancing 4 Enhancement Method of module provided by the invention is as follows：
First, the coloured image of acquisition is converted into gray level image；
Then, the gray level image is filtered using median filter method：
G (x, y)=Med { f (x-k, y-l) }, (k, l ∈ W)；
Wherein, f (x, y) is original image, and g (x, y) is the image after medium filtering, and W is two dimension pattern plate, and Med is extraction
Median operation functional symbol；
Finally, image equilibration：
The frequency that k-th of gray level of image occurs before calculating equalization processing：
Ps(Sk)=nk/ n, l≤k≤N；
Wherein, nk is k-th of gray-level pixels number, and n is sum of all pixels, and N is gray level sum；
The gray value cumulative distribution function of gray level image before calculating equalization processing：
Pixel value in gray level image is adjusted, keeps the gray value cumulative distribution function of gray level image after equalization processing full
Foot：
Wherein, M is the sum of all pixels of gray level image.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement etc., should all be included in the protection scope of the present invention made by within refreshing and principle.
Claims (3)
1. a kind of non-linear Architectural Design adjusts system, which is characterized in that the non-linear Architectural Design adjusts system and includes：
Environmental factor acquisition module, main control module, nonlinear images make module, nonlinear images enhancing module, building shape
Module is presented in generation module, VR；
Environmental factor acquisition module, connect with main control module, the influence factors such as landform, temperature, humidity for acquiring environment；
Main control module makes module, nonlinear images enhancing module, building shape with environmental factor acquisition module, nonlinear images
Body generation module, VR present module connection, the data information for environmental factor acquisition module to be transmitted to carry out storage with
Processing analysis；
Nonlinear images make module, are connect with main control module, and the data for the acquisition of combining environmental factor acquisition module pass through
Computer modeling technique produces nonlinear images；
The nonlinear images make the iterative model of the data for projection calculating target image of module, and the formula of iterative model indicates
For：
Wherein, X is the target image, and M is sytem matrix, and G is the data for projection, and i indicates iterations, XiIndicate ith
The iteration result obtained after iteration；λ indicates convergence coefficient, and λ ∈ (0,1), M T indicate the transposition to matrix M；The mesh is set
The initial value of logo image, and utilize the iterative model to each of described target image according to pre-set iterations
Pixel is iterated update, obtains the target image, the current grayvalue of the pixel in the iterative model with it is previous
The gray value Uniform approximat of iteration；The pixel zero setting by gray value in target image less than 0；
Nonlinear images enhance module, connect with main control module, are obtained for eliminating picture noise and being extracted to image border
Take the image being more clear；
Building shape generation module, connect with main control module, and building shape model is established for passing through nonlinear images；
Module is presented in VR, is connect with main control module, for being realized by building shape modelling at vr videos and by vr eyes
The scene of virtual image；
The prior information extraction that module image super-resolution rebuilding is presented in the VR is as follows：
(1) it is that training sample set chooses N group image fritter training samples pair in sinusoidal area imageEvery group of image fritter
Training sample is to comprising a high-resolution sine area image fritter and low resolution sine area image fritter, being arranged dictionary size
For K；
(2) dictionary is initializedOuter circulation iterations n, interior loop iteration number t；
(3) for all N groups image fritter training samples pair, gradient is calculated
GradientIt can be calculated according to following formula；
WhereinTo choose image fritter in the low resolution sine domain sample set of input,For the high-resolution of input
Image fritter is chosen in sinusoidal domain sample set,For rarefaction representation, λ (0≤λ≤1) indicates relaxation factor, and N is hits,
αiIt is indicated for the sparse coding of x：
L indicates quadratic term loss, by asking the minimum of above formula that can optimize Dx,Dy, as follows：
||Dx(:,k)||2≤ 1, | | Dy (:,k)||2≤ 1, k=1, L, K
S.t. indicate constrained in since above formula is not easy to solve, introducing regularization mutually solves, therefore above formula can be changed to：
Here γ (0 λ≤1 ＜) is for balancing the parameter between two formulas；
WhereinzjFor j-th of element of z, Ω indicates the set of all situations of j；
(4) for all N groups image fritter training samples pair, update
(5) after all N groups image fritter training samples are to all having calculated, update
GradientIt can be calculated according to following formula；
WhereinTo choose image fritter in the low resolution sine domain sample set of input,For the high-resolution of input
Image fritter is chosen in sinusoidal domain sample set,For rarefaction representation, λ (0≤λ≤1) indicates relaxation factor, and N is hits,
αiIt is indicated for the sparse coding of x：
L indicates quadratic term loss, by asking the minimum of above formula that can optimize Dx,Dy, as follows：
||Dx(:,k)||2≤ 1, | | Dy (:,k)||2≤ 1, k=1, L, K
S.t. indicate constrained in since above formula is not easy to solve, introducing regularization mutually solves, therefore above formula can be changed to：
Here γ (0 λ≤1 ＜) is for balancing the parameter between two formulas；
WhereinzjFor j-th of element of z, Ω indicates the set of all situations of j；
(6) repeat step (3) to step (5) untilConvergence；
(7) output doubledictionary Dx, Dy。
2. a kind of non-linear Architectural Design method of adjustment of non-linear Architectural Design adjustment system as described in claim 1, special
Sign is that the non-linear Architectural Design method of adjustment includes the following steps：
Step 1 acquires the influence factors such as landform, temperature, the humidity of environment by environmental factor acquisition module；
The data information that environmental factor acquisition module is transmitted to is carried out storage and handles analysis by step 2, main control module；
Step 3, the data that the acquisition of module combining environmental factor acquisition module is made by nonlinear images pass through computer simulation
Technology produces nonlinear images；
Step 4 enhances module elimination picture noise and extracts acquisition to image border being more clear by nonlinear images
Image；
Step 5 establishes building shape model by building shape generation module, and module is presented by building shape mould by VR
Type is fabricated to vr videos and realizes the scene of virtual image by vr eyes.
3. non-linear Architectural Design method of adjustment as described in claim 1, which is characterized in that the nonlinear images enhance mould
Block Enhancement Method is as follows：
First, the coloured image of acquisition is converted into gray level image；
Then, the gray level image is filtered using median filter method：
G (X, y)=Med { f (X-k, y-1) }, (k, l ∈ W)；
Wherein, f (x, y) is original image, and g (x, y) is the image after medium filtering, and W is two dimension pattern plate, and Med is extraction intermediate value
Arithmetic operation symbol；
Finally, image equilibration：
The frequency that k-th of gray level of image occurs before calculating equalization processing：
ps(sk)=nk/ n, 1≤k≤N；
Wherein, nk is k-th of gray-level pixels number, and n is sum of all pixels, and N is gray level sum；
The gray value cumulative distribution function of gray level image before calculating equalization processing：
Pixel value in gray level image is adjusted, the gray value cumulative distribution function satisfaction of gray level image after equalization processing is made：
Wherein, M is the sum of all pixels of gray level image.
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| WO2022257753A1 (en) * | 2021-06-08 | 2022-12-15 | International Business Machines Corporation | Constructing virtual reality designed buildings |
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Application publication date: 20180925 |