CN109086294A - A kind of shoe tree database sharing and management method and system - Google Patents
A kind of shoe tree database sharing and management method and system Download PDFInfo
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Abstract
The present invention relates to a kind of shoe tree database sharing and management method and system, this method, comprising the following steps: is managed in a manner of race's table, and carries out feature selecting using combination decision tree algorithm and construct effective classifier；By shoe last and different footwear standard shoe last models with same or similar structure style, general shoe tree position is established as male parent；Then generation is controlled to each parameter on its basis and derives from type shoe tree, classification integration is loaded into master pattern library.
Description
Technical field
The present invention relates to shoe last model constructing technology, in particular to a kind of shoe tree database sharing and management method and it is
System.
Background technique
People's foot has many characteristics, such as to easily deform and is difficult to remain static, if obtained using contact type measurement equipment
The point cloud data for taking foot, the probe of measuring device can squeeze instep in measurement process, allow people's foot shape to deform, making one
But also people's pin point cloud that measurement obtains generates some errors while foot receives damage.And it is needed using contact type measurement equipment
Very important person's foot is constantly in a certain specific position, and needs to be fixed using equipment on the table, people's foot when measuring
It also needs to follow movable workbench, people's foot is difficult to realize these demands, this results in many foot's characteristic parameters and some complexity
Curved surface, curve can not be all measured, so that people's foot measurement pointcloud data have very big error.In view of safe and healthy
It is required that cannot be hazardous to the human body for measuring the ray of the scanning device of people's pin point cloud, therefore in non-contact measurement
Radiographic imaging method and industrial computed tomography imaging method all cannot be used in the measurement of people's pin point cloud.Process of the present invention uses
The mode shot when non-contact goes out people's foot model by the image reconstruction shot.
And existing shoe last model Reverse reconstruction file format is mostly point cloud model and blendes together method or facet by boundary
The nurbs surface model that characteristic method generates, the model part that these methods obtain can be distorted, and fairness is bad, and there are more superfluous
Yu Dian, storage space is big, and data management and analysis difficulty are big, is unfavorable for the real-time processing of model.
Summary of the invention
It is an object of the present invention to which solving prior art foot shoe last model constructs the existing above problem.
To achieve the above object, on the one hand, the present invention provides a kind of shoe tree database sharing and management methods, including with
Lower step:
It is managed in a manner of race's table, and carries out feature selecting using combination decision tree algorithm and construct effective classifier；
By shoe last and different footwear standard shoe last models with same or similar structure style, general shoe tree position is established as father
This；Then generation is controlled to each parameter on its basis and derives from type shoe tree, classification integration is loaded into master pattern library.
Preferably, the mode in standard shoe tree model data source includes:
Existing shoes type demand is arranged and is divided, by the last size standard of different regions country by Data Integration, root
According to each positional parameter data are manually entered, system calculates data reasonability automatically, and corresponding professional parameter is calculated automatically,
Shoe tree nurbs surface model, dimensioning, detection model specification are generated in the automatic Parametric modeling procedure algorithm of internal system
Property, the calculating analysis report of Model Matching is obtained, category of model integration is loaded into master pattern library；Or
Picture reconstruction material object shoe tree mode, obtains shoe tree point cloud model, pre-processes and extracts characteristic point characteristic curve, closed
Bond parameter, it is automatic to calculate data reasonability, it is bent that shoe tree NURBS is generated in the automatic Parametric modeling procedure algorithm of internal system
Surface model, dimensioning, detection model is normative, obtains the calculating analysis report of Model Matching, and category of model integration is loaded into mark
Quasi- model library；Or
People foot image is shot, foot's point cloud model is obtained from image reconstruction, converts triangle gridding mould for point cloud model
Type pre-processes and extracts characteristic point characteristic curve, is integrally deformed to obtain band with high simulation to model and feature line feature point
Model is worn, key parameter is obtained, it is automatic to calculate data reasonability, most adaptation shoe tree, the positioning of foot last carving are automatically selected in systems
Local directed complete set, adjustment obtains target shoe tree on script standard shoe last model, and dimensioning, detection model is normative, obtains mould
The matched calculating analysis report of type, category of model integration are loaded into master pattern library；Or
By shoe tree cardboard or snap-gauge, it is aligned each cardboard or snap-gauge position, obtains shoe tree side profile, respectively degree of enclosing profile and shoes
Return pulley is wide, and contour curve is loaded into parametric modeling system, inputs shoe tree size and regional population, and system calculates data conjunction automatically
Corresponding professional parameter is calculated automatically, generates shoe tree in the automatic Parametric modeling procedure algorithm of internal system for rationality
Nurbs surface model, dimensioning, detection model is normative, obtains the calculating analysis report of Model Matching, category of model integration
It is loaded into master pattern library；Or
By shoes image reconstruction mode in kind, shoes point cloud model is obtained, pretreatment measures and inputs corresponding position cladding thickness,
Cladding thickness is uniformly removed on the basis of origin cloud model, the point cloud model of removal cladding thickness is pre-processed and extracts spy
Point feature line is levied, key parameter is obtained, it is automatic to calculate data reasonability, in the automatic Parametric modeling procedure algorithm of internal system
Middle generation shoe tree nurbs surface model, dimensioning, detection model is normative, obtains the calculating analysis report of Model Matching, mould
Type classification integration is loaded into the methods of master pattern library.
Preferably, to the different size model constructions of same moulding shoe tree, using calculating target shoe last model and existing ruler
The scale value of code shoe last model, multidimensional scaling, which calculates, to be modified and numbers storage.
On the other hand, the present invention provides a kind of shoe tree database sharing and management systems, including execute following steps:
It is managed in a manner of race's table, and carries out feature selecting using combination decision tree algorithm and construct effective classifier；
By shoe last and different footwear standard shoe last models with same or similar structure style, general shoe tree position is established as father
This；Then generation is controlled to each parameter on its basis and derives from type shoe tree, classification integration is loaded into master pattern library.
Detailed description of the invention
Fig. 1 is that a kind of foot's shoe last model provided in an embodiment of the present invention constructs system structure diagram；
Fig. 2 is the image reconstruction system workflow schematic diagram that foot's shoe last model shown in Fig. 1 constructs system；
Fig. 3 is that the reconstruct foot point cloud model processing system workflow of the building system of foot's shoe last model shown in Fig. 1 is shown
It is intended to；
Fig. 4 is the processing of reconstruct foot triangle grid model and characteristic point ginseng that foot's shoe last model shown in Fig. 1 constructs system
Number automatically extracts and labeling system workflow schematic diagram；
Fig. 5 is the human foot model positioning transformations working-flow schematic diagram that foot's shoe last model shown in Fig. 1 constructs system；
Fig. 6 is foot's characteristic parameter driving standard shoe tree anamorphotic system work that foot's shoe last model shown in Fig. 1 constructs system
Make flow diagram；
Fig. 7 is the reconstruct shoe last model detection system workflow schematic diagram that foot's shoe last model shown in Fig. 1 constructs system；
Fig. 8 is the reconstruct shoe last model output system workflow schematic diagram that foot's shoe last model shown in Fig. 1 constructs system；
Fig. 9 is the personal shoe tree data base management system workflow signal that foot's shoe last model shown in Fig. 1 constructs system
Figure；
Figure 10 is that foot's shoe last model shown in Fig. 1 constructs the shoe tree database sharing of system and management system workflow is shown
It is intended to；
Figure 11 (a)-Figure 11 (c) is foot's feature line feature point schematic diagram；
Figure 12 (a)-Figure 12 (b) is shoe tree characteristic curve schematic diagram.
Specific embodiment
After embodiments of the present invention are described in detail by way of example below in conjunction with attached drawing, of the invention its
His features, characteristics, and advantages will be more obvious.
Fig. 1 is that a kind of foot's shoe last model provided in an embodiment of the present invention constructs system schematic.As shown in Figure 1, this is
System includes follow shot equipment, processing server, image reconstruction system, reconstruct foot's point cloud model processing system, reconstruct foot
Triangle grid model processing and characteristic point parameter automatically extract simultaneously labeling system, human foot model positioning transformations system, shoe tree data
Library building and management system, foot's characteristic parameter driving standard shoe tree anamorphotic system, reconstruct shoe last model detection system, personal shoes
Last carving data base management system and reconstruct shoe last model output system.
Fig. 2 is the image reconstruction system workflow schematic diagram that foot's shoe last model shown in Fig. 1 constructs system.Such as Fig. 2 institute
Show, image reconstruction system workflow setting are as follows:
People places object of reference when level is stood, on foot side, and foot is contacted object of reference, is placed on object of reference naturally,
Using follow shot equipment around foot shoot one group of two dimensional image or around foot shoot one section of complete video as far as possible every
Change angle is between 10 degree to 45 degree within one second.It is required that mobile device shoots pixel in 200pdi or more.Mobile device passes through net
Network will shoot gained image graphic and upload to processing server.It is generally difficult to obtain whole foot's letters by a sub-picture
Breath, needs to obtain the image at each position of people's foot, then whole people's foot 3D point cloud number is obtained after being merged by registration algorithm
According to.
Gray processing processing is carried out for the image of upload, the specific steps are color is divided into R, tri- Color Channels of G, B are right
In different colorations, brightness, the pixel of saturation degree indicates white brightness with Y, calculates the brightness value of each pixel, makes brightness
Value and its R, G, B value is corresponding, obtains gray level image.And gray processing processing is equally carried out for video, a frame was taken every one second
Image is as reconstruction model reference picture.
The pixel value for belonging to background parts is zeroed by the threshold value that people's foot and background can be reasonably distinguished by setting
(or being maximized) retains the pixel value for belonging to people's foot part.Multiple scale detecting angle point technology is used based on gray level image, quickly
It extracts characteristics of image angle point and finds corresponding relationship in the image of the overlapping of Same Scene, and different images are carried out by reference
Scaling on the basis of object size.
First by forming image resolution pyramid, to determine the characteristic point in each image for changing resolution ratio.
It is denoised with SIFT algorithm smoothed image, gray level image is used to the N-dimensional space normal distribution equation of Gaussian blurring function:Wherein σ indicates Image Smoothness, that is, normal distyribution function standard deviation, as σ is incremented by, resolution ratio
Successively decrease, r is blur radius, and blur radius refers to template elements to the distance of template center.By Gaussian blurring function G(x,y,δ)With
Original image I(x,y)The scale-space representation formula of the image: L is obtained using convolution algorithm(x,y,δ)=G(x,y,δ)*I(x,y).Image is done
The Gaussian Blur of different scale, the method that resolution ratio successively reduces down-sampled i.e. are sampled smoothed image, building Gauss gold
Word tower and difference of Gaussian pyramid determine the position for indicating image Harris corner characteristics in pyramidal each layer.
By finding the position of each corner characteristics, the angle intensity is shown, according to angle intensity and predetermined minimum intensity threshold pair
Than, point is deleted, retest determines whether sum is more than maximum permissible value, if being more than, increases radius and repetitive process again,
Alternately, until Angle Position number equals or falls below maximum permissible value.
By using the method for blur gradients, each corner characteristics distribution orientation is given.First angle point is detected under large scale,
Then real characteristic angle point relatively precisely determine under smaller scale.
Because Gaussian kernel is unique linear kernel, that is to say, that other will not be introduced by, which being obscured using Gauss collecting image, makes an uproar
Sound, therefore selected Gaussian kernel just to construct the scale of image.Harris angle point operator is turned with the first derivative of Gaussian kernel
Become scale space operator, for scale space operator to the analysis of image not by image grayscale level, the influence of contrast variation is full
Sufficient translation invariance, scale invariability, Euclid's invariance, the characteristics such as affine-invariant features.
For the more position of precise positioning characteristic point and scale, redundancy is removed by being fitted the method for three-dimensional quadratic function
Characteristic point and unstable skirt response point improve noise resisting ability to enhance matching stability.
It carries out curve fitting to scale space difference of Gaussian function, original position is obtained plus the offset and scale of fitting
Obtain the exact position of characteristic point.Wherein fitting function are as follows:
X=(x, y, δ)T, derivation simultaneously allow functional equation be equal to 0, obtain extreme value
Point offset expression formula are as follows:Corresponding extreme point expression formula are as follows:When extreme point is inclined
Shifting amountWhen, palpus changes current signature point position, and interpolation is until convergence repeatedly in a new location, if changing beyond setting
Generation number exceeds image boundary range, then deletes the point.Simultaneously in order to avoid D(x)Functional value is too small and is caused by noise jamming
Wild effect, | D(x)|≤0.03 extreme value point deletion.
After obtaining characteristic point position, local principal curvatures is acquired by Hessian matrix (Hessian matrix), it is assumed that Hessian
Matrix expression are as follows:The sum of diagonal of a matrix is Tr(H)Its expression formula are as follows: Tr(H)=Dxx+Dyy=alpha+beta,
The determinant of matrix H is Det(H), expression formula Det(H)=DxxDyy-(Dxy)2=α β.Curvature threshold is set as γ, by sentencing
It is disconnectedIt is whether true, retain marginal point if setting up, on the contrary it deletes, judge whether principal curvatures is setting with this
Under fixed threshold value.KD-tree algorithm acceleration search is used in the process.
The Sobel operator for reusing Canny edge detection algorithm calculates the difference Gx of gradient calculating both horizontally and vertically
And Gy, calculate gradient-norm and direction:θ=atan2 (Gy,Gx), gradient angle, θ ∈ [- π, π], by its approximation
To four direction, level is respectively represented, vertical and two diagonals (0 °, 45 °, 90 °, 135 °).It can be with ± i π/8 (i
=1,3,5,7) divide, fall in the gradient angle in each region to a particular value, represent one of four direction.
The gradient edge more than one pixel usually drawn is wide, but multiple pixels are wide.Therefore gradient map as is also
It is very fuzzy.In order to meet only one accurate point width of edge, edge is refined using non-maximum value suppressing method, helps to protect
It stays local maxima gradient and inhibits every other gradient value, only remain position most sharp keen in change of gradient.Algorithm is as follows:
Compare the gradient intensity of current point and the gradient intensity of positive and negative gradient direction point.If the gradient intensity of current point and
It is maximum that the gradient intensity of other equidirectional points, which compares, retains its value.Otherwise inhibit, that is, be set as 0.
Front we gradient direction approximation to level, vertical and two diagonal line four directions, so each pixel root
It is compared according to itself direction in one of this four direction, decides whether to retain.
Canny algorithm application bivalve value, i.e. a high threshold values and a low valve valve distinguish edge pixel.If edge picture
Vegetarian refreshments gradient value is greater than high threshold values, then is considered as strong edge point.If edge gradient value is less than high threshold values, it is greater than low valve valve,
Then it is labeled as weak marginal point.Point less than low valve valve is then suppressed.
The 8 connection field pixels for checking a weak marginal point with hysteresis bounds track algorithm again, as long as there is strong edge point to deposit
, then this weak marginal point be considered as really edge remain.
The weak edge of all connections is searched for depth-priority-searching method, if one connection weak edge any one point and
The connection of strong edge point, then retain this weak edge, otherwise inhibit this weak edge.
After obtained edge contour, the stabilising direction of partial structurtes is sought using the resulting image gradient of previous step, is used
The gradient-norm of pixel and direction in cake chart statistics field are segmented by 10 degree, and the maximum region direction of cake chart represents feature
The principal direction of point, in order to enhance matched robustness, Retention area region is greater than the auxiliary direction of conduct of principal direction 80%.Spy
Sign point copies as the characteristic point of segmentation, and direction value is assigned to the characteristic point after duplication respectively.Finally to discrete gradient side
Interpolation fitting processing is carried out to histogram, acquires more accurately deflection angle value.
Each characteristic point establishes a descriptor, with one group of vector description, describes sub- use in characteristic point scale space
The gradient information in 8 directions calculated in 4*4 window, total 4*4*8=128 dimensional vector characterization.The size of each subregion and spy
Identical when sign point direction distribution, i.e., there is N_otc pixel in each region, then distributes the square that side length is N_otc for each subregion
Shape region is sampled.Bilinear interpolation is used again, is determined and is calculated the required image-region of description, reference axis is rotated to be
The direction of characteristic point to ensure rotational invariance, then the sampled point in field is assigned in corresponding subregion, by subregion
Interior gradient value is assigned on 8 directions, calculates its weight, then interpolation calculation 8 direction gradients of each seed point.Finally carry out
Normalized counts 128 dimension SIFT local descriptions of each characteristic point, the as feature vector of characteristic point.
Euclidean distance by describing son between two characteristic points of searching is closer to, and the Euclidean between other feature point descriptions
Apart from farther away characteristic point between as the candidate feature matching pair two images.By each characteristic point matched in obtained image
Coordinate is indicated with homogeneous coordinates, and homogeneous coordinates are standardized expression.x1,x2∈R3xn, n
For pairing sum, x1,x2For match point a pair of of in two images.Standardize x1,x2Homogeneous coordinates after:Wherein uxjAnd uyjRespectively indicate xij, yijBe averaged
Value, δxjAnd δyjRespectively indicate xij, yijStandard deviation.Homogeneous coordinates matrix quantificational expression is obtained by homogeneous coordinates again:
Wherein abcdrfghi indicates that rotation and scaling matrix, lmn indicate translation square
Battle array, pqr indicate that projection matrix, s indicate overall conversion matrix.Obtain x1,x2Two shape Interactive matrix, expression formula are as follows:Calculated by calculating and comparing Euclidean distance two shape Interactive matrix by column between difference,
And arrange the Euclidean distance between each column vector by descending descending, threshold value point of cut-off is set, Euclidean distance value is greater than
The matching of point value is truncated to deletion in threshold value, leaves correctly matching pair.
For three-dimensional point cloud model, indicatrix extraction process is exactly that the data point on model is analyzed and counted
It calculates, finds out characteristic point therein, and connect and compose smooth features curve, uneven, shortage topology between point is issued for data point
The point cloud data of link information carries out Gauss Discrete Mapping to each point in point cloud data with discrete Gauss Map method,
Mapping point is subjected to the coagulation type hierarchical clustering based on distance with K-meam algorithm, cluster result and curvature are analyzed,
And selected with parameter of the adaptive iterative process to algorithm, optimal feature point set is obtained, the identification feature comprising before
The sharp mutable site characteristic point being difficult in point step.Then use the characteristic curve based on PCA principal component analytical method raw
Long algorithm, characteristic point is connected, and obtains the characteristic curve of fairing.PCA Principal Component Analysis Algorithm is finally used, to the data obtained sample
Dimensionality reduction compression is carried out, storage memory is reduced.
Fig. 3 is that the reconstruct foot point cloud model processing system workflow of the building system of foot's shoe last model shown in Fig. 1 is shown
It is intended to.As shown in figure 3, reconstruct foot's point cloud model processing system workflow are as follows:
By obtaining having compared with precise positioning, direction, feature vector, the point of descriptor by two-dimensional images matching reconstruct
Cloud threedimensional model.But it generally can not be directly as people's foot 3D model data, at this time by the 3D point cloud data that picture reconstruction obtains
Obtained point cloud model cannot be directly used to parameter extraction and reconstructed surface, need to carry out some pretreatments also to reduce model not
Foot.
Since the 3D point cloud data of acquisition are inevitable that noise (i.e. outlier) exists, simultaneously because human body
Between block and will appear cavity, in addition the flatness of point cloud data is also poor.It needs to simplify point cloud model, and is removed
Discrete point, the processing of smooth and holes filling.
Fragment first is carried out to point cloud model with the method for spatial level subdivision, one is calculated for each fragment and simplifies expression,
The point cloud model being simplified.
With string high differentiation, the front and back two o'clock of connecting detection point, the distance of technology intermediate data points to string, by this distance
It is compared with the franchise value being given to, is then abnormal point if more than franchise value, is deleted, so that density height in point cloud model is removed,
The noise in the biggish place of Curvature varying.Weighted median filtering method is used again, eliminates burr, smoothing model.
By calculating the uniform hole boundary of length of each edge, if it exceeds 2 times of equalization points are away from then taking its midpoint to be added to
In hole boundary.Unified boundary direction, hole boundary is all unified for counterclockwise.Inner and outer boundary is judged, to outer boundary
Profile carries out judgement removal.The concavity and convexity for being closed each vertex in hole boundary is different, and angle calcu-lation mode is different, each according to calculating
Filling point coordinate is calculated in the size of angle, and the illegal judgement point fallen in outside perforated is removed, can be obtained
Filling point coordinate.The iterative cycles process, until new filling point cannot be calculated, then holes filling terminates.
Fig. 4 is the processing of reconstruct foot triangle grid model and characteristic point ginseng that foot's shoe last model shown in Fig. 1 constructs system
Number automatically extracts and labeling system.As shown in figure 4, the processing of reconstruct foot's triangle grid model and characteristic point parameter automatically extract simultaneously
Labeling system workflow are as follows:
To treated, point cloud model uses Waston algorithm to carry out triangulation and optimization, by point cloud model triangle gridding
Change, increases the topological relation between a cloud, especially in deformation process, triangle mesh model while retaining point cloud data again
It has great advantages.Three-dimensional grid sequence compaction is carried out with the deformation of surface technology based on thin plate spline again, passes through MC algorithm
Carry out three-dimensionalreconstruction.MC algorithm uses the tri patch of triangle band connection dispersion, reduces EMS memory occupation；Using reduction tri patch
Quantity, reduce time and space complexity；Discrete noise region is filtered using connectivity detection, removes impurity；Finally examine
Hole is measured, and by filling-up hole to model process of refinement.
Fig. 5 is the human foot model positioning transformations working-flow schematic diagram that foot's shoe last model shown in Fig. 1 constructs system.
As shown in figure 5, human foot model positioning transformations working-flow are as follows:
Obtain the model that the non-fluidity triangle gridding of foot is constituted, but the acquisition of the non-fluidity triangle grid model of foot type
It is the two-dimentional foot type image reconstruction gained original based on stereoscopic vision, the standard shoe tree in human foot model and shoe tree database is all
Defined in respective local coordinate, coordinate origin, change in coordinate axis direction is different, and spatial position is entanglement, copes with foot
Model carries out orientation adjustment according to the shoe last model space coordinate benchmark of system, adapts it to this process identifying system.Figure 11
(a)-Figure 11 (c) is foot's feature line feature point schematic diagram, specific implementation step are as follows: extracts the foot of foot's triangle grid model
Return pulley profile identifies waist position inside and outside sole with foot type sole contour line characteristic trend existing in database, inside and outside sole position
Waist most salient point is respectively C1C2, and waist most salient point is respectively B1B2 inside and outside heel region followed by peripheral point is B, half sole first
Toe endpoint is A, is then sequentially connected AC1B1BB2C2, forms complete closed curve, the as preliminary contour line of sole.Then
It is separately connected C1C2, B1B2, B point makees straight line excessively and line segment B1B2 intersects at point D1, adjustment BD1 and line segment B1B2 corner dimension,
Make 96 degree of angle B1D1B, extend BD1 and intersect at point D2 with line segment C1C2,.It crosses half sole the first toe terminal A and is line segment BD2 and prolong
The vertical line of long line simultaneously meets at point A1, then line segment A1B is sole center line, and the Euclidean distance between point A1 and point B is sole
Length.Foot's broadside lines of foot's triangle grid model is similarly extracted, it is special with foot type broadside lines existing in database
Sign trend, identification heel position of center line curve are l1, and instep position of center line curve is l2, are sequentially connected curve
l1A1Bl2.Make point B as coordinate origin, line segment A1B is in x-axis principal direction, curve l1l and the line segment A1B curve connecting and xz
Plane is parallel, and line segment B1B2 and line segment C1C2 are parallel with x/y plane, and triangle grid model is moved integrally adjustment orientation, model
Coordinate system is matched with rectangular coordinate system in space, obtains the coordinate value of individual features point.
Half sole width C 1C2 followed by width B1B2M are obtained by spatial value, the long A1B of foot, (midpoint l2 z is sat instep height
Scale value), the first toe height, the data such as ankle height.Thus in standard shoe tree number library, by manually selecting with height, shoes money
Type (sport footwear, playshoes, high-heeled shoes, sandals etc.), gender, the options such as area, then the foot's triangle reconstructed by automatic identification
Characteristic point characteristic curve data on grid model are selected with user's foot Model Matching degree of reconstruct most according to standard foot last carving difference
High shoe tree group, the standard shoe tree that system Auto-matching is most adapted to.Because completely the same shoe tree is not most with foot type size
Comfortable shoe tree interferes proper motion in order to avoid shoes are tightly attached to people's foot, needs to reserve some surpluses of putting in shoes and holds with rear
Difference, general last carving enclose=bottoms-feel difference.Exist only between foot and shoe tree and put surplus and dimension difference, could preferably lock
Firmly foot does not have extruding sense of discomfort again.
After selection obtains matched standard shoe tree, although human foot model has pressed shoe tree spatial coordinate system in step before
Orientation adjustment is carried out, but there are still spatial position dislocation in local location, need to carry out space measurement Data Position to foot last carving model
Alignment, including adjustment foot type heel and shoe tree followed by be aligned, adjustment foot type is integrally evenly distributed in last carving body the right and left, adjustment foot
Bottom touchdown point and last carving bottom land point contact, but the step for during do not change pair of the foot type model integrally between each position
It should be related to, benchmark is transformed to whole unified rotation translation.
For the human foot model after realignment, the long i.e. line segment A1B length of foot is being intercepted respectively along positive direction of the x-axis (with millimeter
For unit) 25% and 68% point be set as point F1, F2, then choose that the long point abscissa of 68% foot increases 15mm and abscissa is reduced respectively
The two o'clock of 20mm is set to point F3, F4, draws one respectively perpendicular to the auxiliary of x-axis in tri- data points of F1F3F4 of interception
Alignment line and the auxiliary rotational circle for being parallel to xz plane are helped, and in auxiliary line position respectively by foot's triangle grid model, sole
Contour line and foot broadside lines are divided into four parts, by the step for the contour line that obtains and foot's triangle grid model by not
Form four groups with position, the auxiliary line of same position and auxiliary circle formed into a group, the step for secondary alignment
Line is parallel with z-axis and intersects with x-axis, assists the intersection point of the center of circle of rotational circle co-located secondary alignment line and x-axis.
According to the standard shoe tree that matching obtains, and the back height value (in inches) of selection, previous step is set
The secondary alignment line set and auxiliary rotational circle position, carry out profile line segment rotation and translation by certain pivot rule, and rotation is arranged
The minimum and maximum limit value of gyration.Being located at F3 point rotation angle is α, is β in F4 point rotation angle, rotates angle in F1 point
For γ, F3 is region 1 along positive direction of the x-axis curved portion, and F3 is region along positive direction of the x-axis intersection along negative direction of the x-axis and F4
2, F1 along negative direction of the x-axis and F1 be region 3 along positive direction of the x-axis intersection, and F1 is region 4 along negative direction of the x-axis part.First will
Region 1 using point F3 as rotary middle point auxiliary rotational circle on rotated counterclockwise by angle α (degree),α
∈[2,8].Then region 3 is rotated clockwise into angle beta (degree) on auxiliary rotational circle using point F4 as rotary middle point,It then is rotation with point F2 by region 4
Central point rotated counterclockwise by angle γ (degree) on auxiliary rotational circle,The region rotated introversion line segment is translated
Make between different zones line segment original tie point as close as possible to, using the crosspoint between different zones inner curve as point of cut-off to curve into
Row trimming, the curve being retained in region, then will be smoothly connected between each curved section with second order parameter continuity, similarly will not
It is trimmed and is smoothly connected with the curved surface in region.
Fig. 6 is foot's characteristic parameter driving standard shoe tree anamorphotic system work that foot's shoe last model shown in Fig. 1 constructs system
Make flow diagram.As shown in fig. 6, foot's characteristic parameter drives standard shoe tree anamorphotic system workflow are as follows:
Horizontal standing human foot model is converted into band with high human foot model, and obtains band with the profile of high human foot model
Curve.Purpose for human foot model deformation is primarily to make foot type and shoe tree camber ratio more consistent, convenient for foot last carving model
Analysis of Human Comfort and data inspection correction, while the exposed human foot model laid flat being allowed to be adjusted to closer to making when wearing shoes
Type radian.
Each position degree of enclosing line is imported, band is utilized into minimum two with the contour curve of high human foot model and standard shoe last model
Multiplication and conventional method matched curve, using the contour line of human foot model as the contour line parameter of benchmark adjustment criteria last carving model,
One circle contour line is divided into 360 points, wherein closing on 3 points of polar radius minute by parameter spacing between comparative silhouette line and degree of enclosing line
Not are as follows: ρi+1,ρi,ρi-1, intermediate polar radius value isIf ρiCorresponding polar radius is ρ after fairingi',
The triangular apex obtained after fairing is ρ, the pole half after Fair Fitting is sought with minimizing energy method formula combination polar radius value
Diameter point:Wherein in triangles of the θ for 3 points of compositions, while meeting minimum two
Multiplication:It is forced using weight factor controlling curve fairness and with human foot model parameter
Nearly property generates final shoe tree control line.
Last surface is generated according to the new shoe tree control line of generation, it will system mouth line and last carving using back arc line and last carving lineback
Return pulley profile is divided into two parts respectively, by adjacent contours connection, forms three four borderline regions.It imports in previous step and adjusts
Whole good new degree of enclosing line, is trimmed to three parts for each position degree of enclosing line last carving return pulley profile and last carving lineback.Last carving bottom is used first
Contour line and last carving bottom point degree of enclosing line, constitute the last carving bottom surface for having the direction z radian.System mouth line and last carving lineback intersection are chosen again
Last carving return pulley profile is divided into two parts by degree of enclosing line, then by last carving lineback, back arc line, last carving return pulley profile, system mouth line and the position
The adjacent contours connection of degree of enclosing line is set, five four borderline regions are formed.Then using in curved surface of last region boundary line and degree of enclosing
Line constitutes various pieces curved surface.
Connection based on surface boundary continuity and fairness is carried out to the curved surface that above-mentioned steps obtain, makes adjacent curved surface side
Boundary line all has same points and order, and adjacent curved surface is made to meet second order continuity in boundary, and singular point or extra is not present
Inflection point, Curvature varying is smaller between curved surface.Make to be mutually linked between curved surface simultaneously, the curvature portion of extension is repaired along intersecting lens
It cuts.Curved surface ideal not enough for fairing effect, by regulate and control curved surface control point, make control point correspond and uniformly
Distribution, and then the more fairing of the curved surface of last of each region is stitched together.Resulting each position curved-surface structure line is parameterized
Mode stores, and convenient artificial change is corrected while reducing calculation amount caused by adjustment next time local curve.
The closing that the curved surface being smoothly connected and last carving bottom surface and system mouth face are stitched into a hypostazation is whole, thus obtains
Shoe last model based on foot's picture reconstruction model, and the shoe tree master pattern for adjusting, can be according to choosing different shoes
Class, shoes money, by the parameter corresponding relationship between different type standard shoe tree, carry out corresponding tune with height on the shoe last model of reconstruct
It is whole.Change for different shoe last styles can recognize the shoe last and last carving status secant profile, generally toe of script shoe last model
Degree of enclosing line divides shoe last model, then identifies that target shoe last model boundary connects contour line, by the last carving body model and target after segmentation
Shoe last model carries out elastic deformation connection, removes seam using three-dimensional smoothing algorithm, is smoothly connected.The curved surface that will be smoothly connected
The closing for being stitched into a hypostazation with last carving bottom surface and system mouth face is whole, to obtain the customized shoe-last mould of different shoe last styles
Type.
Fig. 7 is the reconstruct shoe last model detection system workflow schematic diagram that foot's shoe last model shown in Fig. 1 constructs system.
As shown in fig. 7, reconstruct shoe last model detection system workflow are as follows:
Calculating human foot model measures the distance and angle between the shoe last model corresponding points of building, and maximum detecting distance is arranged,
Maximum angle is compared the distance obtained and angle is calculated with the allowable error value of setting, and measurement obtains maximum positive normal
Deviation and maximum negative Norma l deviation, evaluate local error distribution consistency degree, if the maximum value being calculated is than the permission of setting
It is worth small, shows that shoe tree generated meets the requirements, otherwise just needs to modify to the shoe tree of generation, be met the requirements until generating
Shoe tree.
Category of model integration is loaded into master pattern library and is stored in personal shoe tree database, the pipe based on shoe tree database
Reason mode and data analysis, timely update and safeguard personal shoe tree database, and can be by different brands in shoe tree database
Different model shoes money shoe last model parameter is analyzed with the collocation degree of personal similar shoes money shoe last model parameter, obtains comfort level
Report, is shown by interactive interface, is facilitated user to choose the shoes being more comfortably more suitable for and is carried out.
Fig. 8 is the reconstruct shoe last model output system workflow schematic diagram that foot's shoe last model shown in Fig. 1 constructs system.
As shown in figure 8, reconstruct shoe last model output system workflow are as follows:
The foot type shoe last model completed for building can be converted directly into non-fluidity grid model format, utilize 3 D-printing
Machine carries out rapid shaping.Thimble can also be added in shoe last and last carving heel point position by system, and model group is combined into entity,
Numerical Control Simulation processing is carried out, is exported to meet the file format of CAM processing and obtaining NC numerical control code.It can also be by extracting model wheel
Profile obtains the contour line in three-view diagram direction, and output printing or laser engraving are shoe tree snap-gauge and cardboard, for model check and correction.
Fig. 9 is the personal shoe tree data base management system workflow signal that foot's shoe last model shown in Fig. 1 constructs system
Figure.As shown in figure 9, personal shoe tree data base management system workflow are as follows:
Maintenance for personal shoe tree database, main method are as follows: file parameters maintenance is carried out to existing model, on basis
When foot's reconstruction model and constant basic shoe last model, increases or modification model parameter updates existing according to Rational Parameters analysis
There is model parameter, cancels that original model is old or wrong parameter.It can be carried out by combination decision tree algorithm in filtration parameter
Feature selecting and direction selection, filtering are cancelled model parameter, are no longer stored in individual subscriber shoe tree database.Group is used simultaneously
Decision Tree algorithms are closed, many huge trees are generated to objective attribute target attribute parameter, then basis finds the statistical result of each attribute
The biggish character subset of information content.If the character subset of some often changes, and some character subset difference in change
Be worth very little, then using the character subset infrequently changed as fixed character, more multioperation when increasing every time or modifying model parameter
The character subset group often changed is left in space for, reduces the operational analysis time.
Figure 10 is that foot's shoe last model shown in Fig. 1 constructs the shoe tree database sharing of system and management system workflow is shown
It is intended to.As shown in Figure 10, shoe tree database sharing and management system workflow are as follows:
Building and management for shoe tree database construct shoe tree data base administration by way of parametrization management
System.Figure 12 (a)-Figure 12 (b) is shoe tree characteristic curve schematic diagram, construction method are as follows: by manually selecting with height, shoes money type
(sport footwear, playshoes, high-heeled shoes, sandals etc.), gender, the options such as area, then the foot's triangle gridding reconstructed by automatic identification
Characteristic point characteristic curve data (substantially degree of enclosing, the data such as foot length) on model, the standard shoe tree that system Auto-matching is most adapted to.
The wherein foundation of shoe last model database is managed in a manner of race's table, and uses combination decision tree algorithm (Random
Forests it) carries out feature selecting and constructs effective classifier, by shoe last and different shoes with same or similar structure style
Class standard shoe last model establishes general shoe tree position as male parent, is then controlled generation group to each parameter on its basis
Non-hibernating eggs class shoe tree, classification integration are loaded into master pattern library.
The mode in standard shoe tree model data source has: 1. pairs of existing shoes type demands are arranged and are divided, by different regions
The last size standard of country is by Data Integration, and according to each positional parameter data are manually entered, it is reasonable that system calculates data automatically
Property, corresponding professional parameter is calculated automatically, generates shoe tree in the automatic Parametric modeling procedure algorithm of internal system
Nurbs surface model, dimensioning, detection model is normative, obtains the calculating analysis report of Model Matching, category of model integration
It is loaded into master pattern library；2. picture reconstruction material object shoe tree mode, obtains shoe tree point cloud model, pre-processes and extract feature point feature
Line) key parameter is obtained, it is automatic to calculate data reasonability, it is generated in the automatic Parametric modeling procedure algorithm of internal system
Shoe tree nurbs surface model, dimensioning, detection model is normative, obtains the calculating analysis report of Model Matching, category of model
Integration is loaded into master pattern library；3. shooting people foot image, foot's point cloud model is obtained from image reconstruction, point cloud model is converted
For triangle grid model, characteristic point characteristic curve is pre-processed and extracted, model and feature line feature point are integrally deformed to obtain
Band wears model with high simulation, obtains key parameter, automatic to calculate data reasonability, automatically selects most fitting shoes in systems
Last carving, foot last carving position local directed complete set, and adjustment obtains target shoe tree, dimensioning, detection model rule on script standard shoe last model
Plasticity, obtains the calculating analysis report of Model Matching, and category of model integration is loaded into master pattern library；4. by shoe tree cardboard or card
Plate is aligned each cardboard or snap-gauge position, obtains shoe tree side profile, and respectively degree of enclosing profile and sole profile, contour curve is loaded into
Parametric modeling system, inputs shoe tree size and regional population, system calculate data reasonability automatically, is calculated automatically corresponding
Professional parameter, in the automatic Parametric modeling procedure algorithm of internal system generate shoe tree nurbs surface model, label ruler
Very little, detection model is normative, obtains the calculating analysis report of Model Matching, and category of model integration is loaded into master pattern library；5. by
Shoes image reconstruction mode in kind obtains shoes point cloud model, and pretreatment measures and inputs corresponding position cladding thickness, in origin cloud mould
Cladding thickness is uniformly removed on the basis of type, and the point cloud model of removal cladding thickness is pre-processed and extracts feature point feature
Line obtains key parameter, automatic to calculate data reasonability, generates shoes in the automatic Parametric modeling procedure algorithm of internal system
Last carving nurbs surface model, dimensioning, detection model is normative, obtains the calculating analysis report of Model Matching, category of model is whole
It closes and is loaded into the methods of master pattern library.
In order to enrich shoe tree database data while not increase input step and processing inside, not to same moulding shoe tree
With size model construction, using the scale value for calculating target shoe last model and existing size shoe last model, multidimensional scaling, which calculates, to be repaired
Change and numbers storage.The grade of this step put operation is realized by three-dimension varying matrix, keep with stilt degree it is constant in the case where
Model overall dimensions degree of enclosing is allowed to reach scaling standard.Three-dimension varying matrix includes: spin matrix, the geometric transformation square of equal proportion
Battle array, translation matrix, whole projection matrix etc..Building for different shoes money shoe last models, using in existing shoes money customized shoe-last
On the basis of, with different shoes money shoe trees by corresponding relationship, customized shoe-last, corresponding adjustment local size structure and scaling are docked,
Style can be adjusted according to standard pattern model splicing.
Dimensioning content includes length of last sole degree, back height, preceding to lift up height, half sole touchdown point followed by touchdown point, foot
Back height, mouth width degree of uniting, open height of uniting, mouth length of uniting, toe degree of enclosing, sole degree of enclosing, foot waist dimension, instep degree of enclosing, ankle enclose
Degree etc..
Foot last carving Analysis of Human Comfort and inspection generate cross-section comparison's number at each position according to the foot last carving model matched
According in conjunction with shoe tree comfort level rule, the mean square deviation of otherness, makes analysis between calculating last carving critical size and foot critical size
With inspection.
Using Parametric designing, the pass between size and size is established with the method for mathematical relationship or logical relation
System can shorten the time of modification model.Unified model type is sorted out respectively and is managed.Detection model is normative simultaneously, obtains
The calculating analysis report of Model Matching facilitates modification and correction, improves shoe tree database data confidence level.
The method that the embodiment of the present invention drives shoe tree parametric reconstruction by using characteristic point passes through controlling feature line and control
The parameters relationship of point is made to complete the parametric modeling of shoe tree, the control parameter by modifying shoe tree can complete new shoes model
Building.And to people's foot, the extracting method of shoes and shoe tree characteristic parameter is studied, using the characteristic parameter Controlling model simplified
Generation, and complete the rational inspection of model parameter, the inspection etc. of reconstruction model matching.Tune is stored in a manner of parametrization
It is generated with shoe last model data, while with driving parameter model, model modification is facilitated to update and analysis management.
It should be noted that above-described embodiment is only used to illustrate structure and its working effect of the invention, and it is not used as
It limits the scope of the invention.One of ordinary skilled in the art is right without prejudice to thinking of the present invention and structure
The adjustment or optimization that above-described embodiment carries out, should regard as the claims in the present invention and be covered.
Claims (4)
1. a kind of shoe tree database sharing and management method, which comprises the following steps:
It is managed in a manner of race's table, and carries out feature selecting using combination decision tree algorithm and construct effective classifier；
By shoe last and different footwear standard shoe last models with same or similar structure style, the conduct of general shoe tree position is established
Male parent；Then generation is controlled to each parameter on its basis and derives from type shoe tree, classification integration is loaded into master pattern library.
2. the method according to claim 1, wherein the mode in standard shoe tree model data source includes:
Existing shoes type demand is arranged and is divided, by the last size standard of different regions country by Data Integration, according to people
Work inputs each positional parameter data, and system calculates data reasonability automatically, corresponding professional parameter is calculated automatically, in system
Shoe tree nurbs surface model is generated in internal automatic Parametric modeling procedure algorithm, dimensioning, detection model is normative,
The calculating analysis report of Model Matching is obtained, category of model integration is loaded into master pattern library；Or
Picture reconstruction material object shoe tree mode, obtains shoe tree point cloud model, pre-processes and extracts characteristic point characteristic curve, obtain crucial ginseng
Number, it is automatic to calculate data reasonability, shoe tree nurbs surface mould is generated in the automatic Parametric modeling procedure algorithm of internal system
Type, dimensioning, detection model is normative, obtains the calculating analysis report of Model Matching, and category of model integration is loaded into master die
Type library；Or
People foot image is shot, foot's point cloud model is obtained from image reconstruction, converts triangle grid model for point cloud model, in advance
Characteristic point characteristic curve is handled and extracted, model and feature line feature point are integrally deformed to obtain band and wear mould with high simulation
Type obtains key parameter, automatic to calculate data reasonability, automatically selects most adaptation shoe tree in systems, and foot last carving positioning part is adjusted
Whole, adjustment obtains target shoe tree on script standard shoe last model, and dimensioning, detection model is normative, obtains Model Matching
Calculating analysis report, category of model integration be loaded into master pattern library；Or
By shoe tree cardboard or snap-gauge, it is aligned each cardboard or snap-gauge position, obtains shoe tree side profile, respectively degree of enclosing profile and sole wheel
Contour curve is loaded into parametric modeling system, inputs shoe tree size and regional population, it is reasonable that system calculates data automatically by exterior feature
Property, corresponding professional parameter is calculated automatically, generates shoe tree in the automatic Parametric modeling procedure algorithm of internal system
Nurbs surface model, dimensioning, detection model is normative, obtains the calculating analysis report of Model Matching, category of model integration
It is loaded into master pattern library；Or
By shoes image reconstruction mode in kind, shoes point cloud model is obtained, pretreatment measures and inputs corresponding position cladding thickness, in original
Cladding thickness is uniformly removed on the basis of point cloud model, and the point cloud model of removal cladding thickness is pre-processed and extracts characteristic point
Characteristic curve obtains key parameter, automatic to calculate data reasonability, raw in the automatic Parametric modeling procedure algorithm of internal system
At shoe tree nurbs surface model, dimensioning, detection model is normative, obtains the calculating analysis report of Model Matching, model point
Class integration is loaded into the methods of master pattern library.
3. the method according to claim 1, wherein being adopted to the different size model constructions of same moulding shoe tree
With the scale value for calculating target shoe last model and existing size shoe last model, multidimensional scaling, which calculates, to be modified and numbers storage.
4. a kind of shoe tree database sharing and management system, which is characterized in that including executing following steps:
It is managed in a manner of race's table, and carries out feature selecting using combination decision tree algorithm and construct effective classifier；
By shoe last and different footwear standard shoe last models with same or similar structure style, the conduct of general shoe tree position is established
Male parent；Then generation is controlled to each parameter on its basis and derives from type shoe tree, classification integration is loaded into master pattern library.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113744394A (en) * | 2021-11-05 | 2021-12-03 | 广东时谛智能科技有限公司 | Shoe tree three-dimensional modeling method, device, equipment and storage medium |
| CN117150598A (en) * | 2023-09-05 | 2023-12-01 | 上海易立德信息技术股份有限公司 | Family table management and integration method and system for CAD model construction |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1806710A (en) * | 2005-01-18 | 2006-07-26 | 华侨大学 | Method for automatically forming shoe tree model based on foot characteristic parameter |
| CN101721013A (en) * | 2008-10-22 | 2010-06-09 | 温州市鹿艺鞋材有限公司 | Device and method for measuring comprehensive parameters of shoe tree by laser |
| US20100211360A1 (en) * | 2007-09-18 | 2010-08-19 | Magari S.R.L. | Method for calculating the geometry of a last for customised footwear |
| CN102783767A (en) * | 2011-05-18 | 2012-11-21 | 香港纺织及成衣研发中心有限公司 | System for designing customized shoe tree and evaluating comfort thereof |
| CN104921410A (en) * | 2015-07-10 | 2015-09-23 | 李文谦 | Automatic shoe tree parameter predicting method and device based on double models |
-
2018
- 2018-06-13 CN CN201810604207.8A patent/CN109086294A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1806710A (en) * | 2005-01-18 | 2006-07-26 | 华侨大学 | Method for automatically forming shoe tree model based on foot characteristic parameter |
| US20100211360A1 (en) * | 2007-09-18 | 2010-08-19 | Magari S.R.L. | Method for calculating the geometry of a last for customised footwear |
| CN101721013A (en) * | 2008-10-22 | 2010-06-09 | 温州市鹿艺鞋材有限公司 | Device and method for measuring comprehensive parameters of shoe tree by laser |
| CN102783767A (en) * | 2011-05-18 | 2012-11-21 | 香港纺织及成衣研发中心有限公司 | System for designing customized shoe tree and evaluating comfort thereof |
| CN104921410A (en) * | 2015-07-10 | 2015-09-23 | 李文谦 | Automatic shoe tree parameter predicting method and device based on double models |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113744394A (en) * | 2021-11-05 | 2021-12-03 | 广东时谛智能科技有限公司 | Shoe tree three-dimensional modeling method, device, equipment and storage medium |
| CN113744394B (en) * | 2021-11-05 | 2022-03-04 | 广东时谛智能科技有限公司 | Shoe tree three-dimensional modeling method, device, equipment and storage medium |
| CN117150598A (en) * | 2023-09-05 | 2023-12-01 | 上海易立德信息技术股份有限公司 | Family table management and integration method and system for CAD model construction |
| CN117150598B (en) * | 2023-09-05 | 2024-04-02 | 上海易立德信息技术股份有限公司 | Family table management and integration method and system for CAD model construction |
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