基于个性化虚拟人台的服装合体性评价模型研究
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摘要
本文在当前物联网迅速发展的大背景下,研究了基于个性化虚拟人台的服装合体性评价模型。研究内容包括三维人体信息数据的获取、传输和存储,个性化虚拟人台参数的获取与解析,个性化虚拟人台建模技术,虚拟服装建模技术,服装合体性评价模型等。
首先应用数据挖掘技术对女性躯干部位三维扫描数据进行了研究分析。对用[TC]2系统获取的数据进行预处理,提取出躯干部分的点云数据,将清洗后的三维点云数据作为数据挖掘的数据源,运用描述型数据挖掘算法找出躯干部位人体表面的关键点和关键面。运用预测型数据挖掘算法由关键点预测其它点的数据信息,并进行了误差分析。
在数据挖掘结论的基础上,研究开发了基于多张图片的三维人体信息数据获取系统。从指定角度拍摄多张人体照片,通过图像处理、畸变校正、轮廓识别等获取人体轮廓,再通过特征点识别、关键点识别,获取人体躯干表面的关键点,再由关键点预测其它点的数据信息,从而获取三维人体信息数据。根据数据挖掘提取的关联规则,参考应用XML技术,对三维人体信息数据的表示和传输提出了一套切实可行的解决方案,并在Visual Studio 2005环境下对XML文件进行了解析验证。于此构建了三维人体信息数据库模型,并在MS SQL Sever 2005数据库中进行了具体实现。
本文的个性化虚拟人台建模首先要获取能体现虚拟人台个性化特征的参数。针对网络环境下个性化虚拟人台建模的需要,研究个性化虚拟人台参数的获取与解析,通过运用Web Service技术实现个性化虚拟人台的参数获取方案,为构建个性化虚拟人体提供数据支持。最后运用三角面片建模技术建立了个性化虚拟人台。还研究了在个性化虚拟仿真人台上生成领围线、胸围线、腰围线、臀围线、前中线、后中线及侧缝线等基础线,为服装合体性评价做好准备工作。
最后,论文构建了服装合体性三级评价模型,从低级到高级依次为基于服装号型的服装合体性评价、基于人体基础线的服装合体性评价、基于人体曲面的服装合体性评价。该评价模型适用范围广,能够满足电子商务服装网络营销、物联网发展的需要。运算效率高,三级模型层层递进,每层都能将非常不合体的服装筛选列出,避免了每次评价都需要三级模型逐级全部运行,既节省了运算所需资源,也缩短了评价所需的时间。在使用过程中,用户可以选择所要使用的评价级别,运行更加灵活,更能满足不同用户或用户不同的需要。评价结果运用可视化技术将数据转换成图形,可以在人与数据、人与人之间实现图像通信,操作界面更加友好,可操作性强。
论文创新性地完成了以下工作：
1.基于数据挖掘技术的三维人体扫描数据分析
用描述型数据挖掘算法对女性躯干部位三维点云数据进行分析后,得出两个结论：在层维上,女性躯干部位自下而上均分为50层,同时为一阶差分极大值和二阶差分极小值的层有第10、20、29、31、34、36层。第10层位于人体臀围附近,第20层位于人体腰围附近,第29层位于人体下胸围附近,第31层位于人体胸围附近,第36层位于人体上胸围附近,第34层位于胸围线与上胸围线之间。这六层是表示女性躯干部位曲面特征的关键面。在扇维上,正面轮廓、侧面轮廓、侧45°轮廓(或侧135°轮廓),这三个轮廓所在的都经过原点的平面将女性躯干部位等夹角地分为八等分,相邻两个平面的夹角为45°。投影形成这3个轮廓的3组扇面是女性躯干部位曲面特征的关键扇面；用预测型数据挖掘算法对女性躯干部位三维点云数据进行分析后,得出如下结论：每层中的非关键点与关键点之间存在多值依赖关系,非关键点可根据其相邻的3个关键点的值运用线性回归数据挖掘算法进行预测,其误差在可接受范围之内。
2.构建了三维人体信息数据的获取、传输和存储等标准体系
根据对三维人体扫描数据进行数据挖掘的结论研究了用三张照片获取三维人体信息数据的方法。为了使获取的数据精度能够达到服装行业使用的要求,对人体图像的获取和人体图像的畸变进行了校正,对校正后的图像进行轮廓提取、人体特征点识别、人体关键点计算,进而能够实现人体尺寸的提取和生成三维人体点云数据。为了满足数据获取需要制作了角度定位旋转板等辅助工具,开发了可以获取三维人体点云的三维人体信息数据获取系统。应用XML技术,针对三维人体信息数据的表示和传输,参考服装行业相关标准,提出了一套切实可行的解决方案,并在Visual Studio 2005环境下,对XML文件进行了解析验证。构建了三维人体信息数据库模型,并在MS SQL Sever 2005数据库中进行了具体实现。
3.建立了面向服装合体性评价的个性化虚拟服装人台
建立了面向服装合体性评价的个性化虚拟服装人台,按照建立人体模型所需的参数,分为四个层级。参数结构由简到繁依次为一至四层级即尺寸层级、特征点层级、关键点层级、点云层级,各层级之间也可以组合使用。本文使用第三层级即关键点层级,通过Web Service技术建立个性化虚拟人台参数的获取方案,实现了能够适应Internet计算环境的松散耦合的分布式组件互联,可以将存储在不同的商家、存储在不同的操作系统和硬件平台上数据结构和程序接口不同的个性化人体数据有效地结合到一起使用,实现商家之间客户个性化身体数据信息的共享。通过获取的关键点信息生成人体躯干部位点云的三维坐标数据,对点云数据进行网格化处理后,基于三次B样条技术进行了人体曲面建模。最后,为了满足多级服装合体性评价需要,在所建立的个性化虚拟仿真人台上生成了领围线、胸围线、腰围线、臀围线、前中线、后中线及侧缝线等基础线。
4.构建了服装合体性三级评价模型
在服装合体性三级评价模型体系结构中,基于服装号型的服装合体性评价是最低级别的评价。其评价方法最简单,所需数据最少,运算时问最短,能够满足款式较宽松、面料弹性较大的服装的合体性评价需要。二级基于人体基础线的服装合体性评价是在一级评价基础上进行的,只有在一级评价基本通过,并需要做进一步评价的情况下,才进入二级评价流程。三级基于人体曲面的服装合体性评价在二级评价基础上进行,只有在二级评价基本通过,并需要对服装合体性做进一步评价的情况下,再进入三级评价流程。三级评价是最高级别的评价,需要的人体数据和服装数据比较详细,运算时间也相应较长,能够满足款式贴体和较贴体、面料弹性较小的服装的合体性评价需要。该模型适用范围广,满足电子商务服装网络营销、物联网发展的需要。其运算效率高,三级模型层层递进,每层能列出不需要进行高一级评价的服装。既节省了运算所需资源,也缩短了评价所需的时间。在使用过程中,用户可以根据需要选择所要使用的评价级别,运行更加灵活。评价结果运用可视化技术将数据转换成图形,在人与数据、人与人之间实现图像通信,故操作界面更加友好,可操作性强。
In the context of rapid development of Internet of Things, evaluation model of clothes fit based on individual virtual mannequin has been studied in this paper, which includes data acquisition, transmission and storage of 3D body, acquiring and analysis parameters of individual virtual mannequin, modeling of individual virtual mannequin, modeling of virtual clothes, evaluation model of clothes fit, etc.
The first work is application of data mining technology to analysis 3D scanning data of women's torso that is extracted from 3D body data of point cloud, which is acquired by means of [TC]2 system.3D cloud point data that has been cleaned is the data source of data mining. The key points and key planes are obtained by application of descriptive algorithm of data mining. The other points can be obtained by application of forecasting algorithm of data mining. The error analysis has been done.
Based on the conclusion of data mining, the data acquisition system of 3D body based on multi-photo has been developed. The body contours is extracted from images that are captured at designated angle. Image processing and distortion correction must be done in advance. The characteristic points and key points are extracted from body contours, based on which the other points are calculated.3D body data are acquired through the above steps. A feasible solution of representation and transmission of 3D body data is put forward by the application of XML technology according to association rules that have been extracted by data mining. XML documents are interpreted and verified in the environment of Visual Studio 2005. Database model of 3D body is built and implemented in MS SQL Sever 2005 database.
The precondition of individual virtual mannequin is to obtain parameters that can embody the individual features of virtual mannequin. In order to meet the demand of individual virtual mannequin modeling in the network environment, a solution of getting parameters for modeling individual virtual mannequin is put forward by the application of Web Service technology. In the last place, model of individual virtual mannequin is built through tri patch modeling techniques. In addition, many lines of reference such as neck line, bust line, waist line, hip line, front center line, back center line, side seam are built on the model, which is prepared for evaluation model of clothes fit.
In the end of this paper, evaluation model of clothes fit is built, which consists of evaluation model based on size designation, evaluation model based on reference lines and evaluation model based on surface of body according to complexity from low to high. The evaluation model of clothes fit has the applicable scope to be broad, which can meet the requirement of the development E-commerce, fashion online marketing and Internet of Things. The evaluation model is highly efficient and the three sub-models are also the progressive process in which clothes that don't even match can be weeded out, thus the three sub-models are not completing execution which not only save the resources and calculation, but also shorten the time of evaluation. During the procedure of implementation, sub-model is optional for user, which is flexible and can meet all kinds of requests from different users. Information of evaluate result is converted into graphic by using visualization technology. The implement of visual communication between people and data or among people makes the evaluation model have user friendly interface and feasible.
The following work has been innovatively completed in this paper.
1.3D scanning data analysis of women's torso based on data mining
After 3D scanning data of women's torso is analyzed by means of descriptive algorithm of data mining, two conclusions can be drawn. In storey dimension, women's torso is divided into 50 layers evenly spaced from bottom to top. Layer 10, 20,29,31,34,36 are both the maximum value of first order difference and the minimum value of second order difference. Layer 10,20,31 are severally located near the hip line, waist line and bust line. Layer 29,31 are respectively situated near the under bust line and upper bust line. Layer 34 is seated between upper bust line and bust line. The six layers are the key layers that present surface features of women's torso. In fan dimension, women's torso is equally divided into 8 portions by three via origin planes that the frontal direction contour, the lateral direction contour and the rotation 45°direction contour respectively seat. The Angle degree of two nearby planes is 45°. The three group fans that drop shadow to plane of projection are the key fans that present surface features of women's torso. After 3D scanning data of women's torso is analyzed by means of forecasting algorithm of data mining, the following conclusion can be drawn. There is a multi valued dependency relationship between key points and other points on each layer. The value of a non-key point can be predicted according to the value of the three nearby key points by using linear regression data mining algorithm. The error is in the acceptable range.
2. Building standards system of data acquisition, transmission and storage of 3D body
The method of 3D body data acquisition based on the conclusion of 3D body data mining is studied which is by means of three photographs that are captured at designated angle. The acquisition and distortion of body image has been adjusted in order that data accuracy can fulfill the employ of clothing industry. The body contours is extracted from images that are captured at designated angle and have been corrected the distortion. The characteristic point and key points are extracted from body contours, based on which the other points are calculated.3D body data are acquired through the above steps. Accessory appliances such as table turning of angular dimensioning have been fabricated. The data acquisition system of 3D body that can provide point cloud data has been developed. A feasible solution of representation and transmission of 3D body data is put forward by the application of XML technology according to association rules that have been extracted by data mining. XML documents are interpreted and verified in the environment of Visual Studio 2005. Database model of 3D body is built and implemented in MS SQL Sever 2005 database.
3. Building individual virtual mannequin that is used for evaluation model of clothes fit
Individual virtual mannequin that is used for evaluation model of clothes fit is built. The parameters to build individual virtual mannequin are provided in four levels that are size level, characteristic point level, key point level and cloud point level in the order from simple to complex. The parameters of different levels can be combined. The third level named key point level is adopted in this paper. The scheme of acquiring parameter for individual virtual mannequin is built through the use of Web Service technology, which can realize interconnection of loosely coupling distributed component. Both various data structure stored on different operating systems and different hardware platforms of various merchants and diverse program interface of individual body data are availably integrated, which share the individual body data of clients in all merchants.3D coordinate data of point cloud of women's torso are created according to key point data. Surfaces model of body based on Cubic B-spline technology is built after point cloud data being grid processed. In addition, many lines of reference such as neck line, bust line, waist line, hip line, front center line, back center line, side seam are built on the model, which is preparing for evaluation model of clothes fit.
4. Building three levels evaluation model of clothes fit
In the architecture of three levels evaluation model of clothes fit, the first level named evaluation model based on size designation is the minimum level, in which algorithm is easy, the number of data required is small, operation time is less, which is appropriate for evaluation of clothes that are loose in style and have great flexibility in fabric. The second level is evaluation model based on reference lines, which is based on the first level. Only if evaluate result of the first level is provisional accreditation and user selects the option of next level, evaluation model based on reference lines is carried out. The third level is evaluation model based on surface of body, which is based on the second level. Only if evaluate result of the second level is provisional accreditation and user selects the option of next level, evaluation model based on surface of body is carried out. The third level is the maximum level, which needs many detailed data of both body and clothes. Operation time is more. The third level is appropriate for evaluation of clothes that are fitted in style and have small flexibility in fabric. The three levels evaluation model of clothes fit has the applicable scope to be broad, which can meet the requirements of the development E-commerce, fashion online marketing and Internet of Things. The evaluation model is highly efficient and the three sub-models are also the progressive process in which clothes that don't even match can be weeded out, thus the three sub-models are not completing execution which not only save the resources and calculation, but also shorten the time of evaluation. During the procedure of implementation, sub-model is optional for user, which is flexible and can meet all kinds of requests from different users. Information of evaluate result is converted into graphic by using visualization technology. The implement of visual communication between people and data or among people makes the evaluation model have user friendly interface and feasible.
首先应用数据挖掘技术对女性躯干部位三维扫描数据进行了研究分析。对用[TC]2系统获取的数据进行预处理,提取出躯干部分的点云数据,将清洗后的三维点云数据作为数据挖掘的数据源,运用描述型数据挖掘算法找出躯干部位人体表面的关键点和关键面。运用预测型数据挖掘算法由关键点预测其它点的数据信息,并进行了误差分析。
在数据挖掘结论的基础上,研究开发了基于多张图片的三维人体信息数据获取系统。从指定角度拍摄多张人体照片,通过图像处理、畸变校正、轮廓识别等获取人体轮廓,再通过特征点识别、关键点识别,获取人体躯干表面的关键点,再由关键点预测其它点的数据信息,从而获取三维人体信息数据。根据数据挖掘提取的关联规则,参考应用XML技术,对三维人体信息数据的表示和传输提出了一套切实可行的解决方案,并在Visual Studio 2005环境下对XML文件进行了解析验证。于此构建了三维人体信息数据库模型,并在MS SQL Sever 2005数据库中进行了具体实现。
本文的个性化虚拟人台建模首先要获取能体现虚拟人台个性化特征的参数。针对网络环境下个性化虚拟人台建模的需要,研究个性化虚拟人台参数的获取与解析,通过运用Web Service技术实现个性化虚拟人台的参数获取方案,为构建个性化虚拟人体提供数据支持。最后运用三角面片建模技术建立了个性化虚拟人台。还研究了在个性化虚拟仿真人台上生成领围线、胸围线、腰围线、臀围线、前中线、后中线及侧缝线等基础线,为服装合体性评价做好准备工作。
最后,论文构建了服装合体性三级评价模型,从低级到高级依次为基于服装号型的服装合体性评价、基于人体基础线的服装合体性评价、基于人体曲面的服装合体性评价。该评价模型适用范围广,能够满足电子商务服装网络营销、物联网发展的需要。运算效率高,三级模型层层递进,每层都能将非常不合体的服装筛选列出,避免了每次评价都需要三级模型逐级全部运行,既节省了运算所需资源,也缩短了评价所需的时间。在使用过程中,用户可以选择所要使用的评价级别,运行更加灵活,更能满足不同用户或用户不同的需要。评价结果运用可视化技术将数据转换成图形,可以在人与数据、人与人之间实现图像通信,操作界面更加友好,可操作性强。
论文创新性地完成了以下工作：
1.基于数据挖掘技术的三维人体扫描数据分析
用描述型数据挖掘算法对女性躯干部位三维点云数据进行分析后,得出两个结论：在层维上,女性躯干部位自下而上均分为50层,同时为一阶差分极大值和二阶差分极小值的层有第10、20、29、31、34、36层。第10层位于人体臀围附近,第20层位于人体腰围附近,第29层位于人体下胸围附近,第31层位于人体胸围附近,第36层位于人体上胸围附近,第34层位于胸围线与上胸围线之间。这六层是表示女性躯干部位曲面特征的关键面。在扇维上,正面轮廓、侧面轮廓、侧45°轮廓(或侧135°轮廓),这三个轮廓所在的都经过原点的平面将女性躯干部位等夹角地分为八等分,相邻两个平面的夹角为45°。投影形成这3个轮廓的3组扇面是女性躯干部位曲面特征的关键扇面；用预测型数据挖掘算法对女性躯干部位三维点云数据进行分析后,得出如下结论：每层中的非关键点与关键点之间存在多值依赖关系,非关键点可根据其相邻的3个关键点的值运用线性回归数据挖掘算法进行预测,其误差在可接受范围之内。
2.构建了三维人体信息数据的获取、传输和存储等标准体系
根据对三维人体扫描数据进行数据挖掘的结论研究了用三张照片获取三维人体信息数据的方法。为了使获取的数据精度能够达到服装行业使用的要求,对人体图像的获取和人体图像的畸变进行了校正,对校正后的图像进行轮廓提取、人体特征点识别、人体关键点计算,进而能够实现人体尺寸的提取和生成三维人体点云数据。为了满足数据获取需要制作了角度定位旋转板等辅助工具,开发了可以获取三维人体点云的三维人体信息数据获取系统。应用XML技术,针对三维人体信息数据的表示和传输,参考服装行业相关标准,提出了一套切实可行的解决方案,并在Visual Studio 2005环境下,对XML文件进行了解析验证。构建了三维人体信息数据库模型,并在MS SQL Sever 2005数据库中进行了具体实现。
3.建立了面向服装合体性评价的个性化虚拟服装人台
建立了面向服装合体性评价的个性化虚拟服装人台,按照建立人体模型所需的参数,分为四个层级。参数结构由简到繁依次为一至四层级即尺寸层级、特征点层级、关键点层级、点云层级,各层级之间也可以组合使用。本文使用第三层级即关键点层级,通过Web Service技术建立个性化虚拟人台参数的获取方案,实现了能够适应Internet计算环境的松散耦合的分布式组件互联,可以将存储在不同的商家、存储在不同的操作系统和硬件平台上数据结构和程序接口不同的个性化人体数据有效地结合到一起使用,实现商家之间客户个性化身体数据信息的共享。通过获取的关键点信息生成人体躯干部位点云的三维坐标数据,对点云数据进行网格化处理后,基于三次B样条技术进行了人体曲面建模。最后,为了满足多级服装合体性评价需要,在所建立的个性化虚拟仿真人台上生成了领围线、胸围线、腰围线、臀围线、前中线、后中线及侧缝线等基础线。
4.构建了服装合体性三级评价模型
在服装合体性三级评价模型体系结构中,基于服装号型的服装合体性评价是最低级别的评价。其评价方法最简单,所需数据最少,运算时问最短,能够满足款式较宽松、面料弹性较大的服装的合体性评价需要。二级基于人体基础线的服装合体性评价是在一级评价基础上进行的,只有在一级评价基本通过,并需要做进一步评价的情况下,才进入二级评价流程。三级基于人体曲面的服装合体性评价在二级评价基础上进行,只有在二级评价基本通过,并需要对服装合体性做进一步评价的情况下,再进入三级评价流程。三级评价是最高级别的评价,需要的人体数据和服装数据比较详细,运算时间也相应较长,能够满足款式贴体和较贴体、面料弹性较小的服装的合体性评价需要。该模型适用范围广,满足电子商务服装网络营销、物联网发展的需要。其运算效率高,三级模型层层递进,每层能列出不需要进行高一级评价的服装。既节省了运算所需资源,也缩短了评价所需的时间。在使用过程中,用户可以根据需要选择所要使用的评价级别,运行更加灵活。评价结果运用可视化技术将数据转换成图形,在人与数据、人与人之间实现图像通信,故操作界面更加友好,可操作性强。
In the context of rapid development of Internet of Things, evaluation model of clothes fit based on individual virtual mannequin has been studied in this paper, which includes data acquisition, transmission and storage of 3D body, acquiring and analysis parameters of individual virtual mannequin, modeling of individual virtual mannequin, modeling of virtual clothes, evaluation model of clothes fit, etc.
The first work is application of data mining technology to analysis 3D scanning data of women's torso that is extracted from 3D body data of point cloud, which is acquired by means of [TC]2 system.3D cloud point data that has been cleaned is the data source of data mining. The key points and key planes are obtained by application of descriptive algorithm of data mining. The other points can be obtained by application of forecasting algorithm of data mining. The error analysis has been done.
Based on the conclusion of data mining, the data acquisition system of 3D body based on multi-photo has been developed. The body contours is extracted from images that are captured at designated angle. Image processing and distortion correction must be done in advance. The characteristic points and key points are extracted from body contours, based on which the other points are calculated.3D body data are acquired through the above steps. A feasible solution of representation and transmission of 3D body data is put forward by the application of XML technology according to association rules that have been extracted by data mining. XML documents are interpreted and verified in the environment of Visual Studio 2005. Database model of 3D body is built and implemented in MS SQL Sever 2005 database.
The precondition of individual virtual mannequin is to obtain parameters that can embody the individual features of virtual mannequin. In order to meet the demand of individual virtual mannequin modeling in the network environment, a solution of getting parameters for modeling individual virtual mannequin is put forward by the application of Web Service technology. In the last place, model of individual virtual mannequin is built through tri patch modeling techniques. In addition, many lines of reference such as neck line, bust line, waist line, hip line, front center line, back center line, side seam are built on the model, which is prepared for evaluation model of clothes fit.
In the end of this paper, evaluation model of clothes fit is built, which consists of evaluation model based on size designation, evaluation model based on reference lines and evaluation model based on surface of body according to complexity from low to high. The evaluation model of clothes fit has the applicable scope to be broad, which can meet the requirement of the development E-commerce, fashion online marketing and Internet of Things. The evaluation model is highly efficient and the three sub-models are also the progressive process in which clothes that don't even match can be weeded out, thus the three sub-models are not completing execution which not only save the resources and calculation, but also shorten the time of evaluation. During the procedure of implementation, sub-model is optional for user, which is flexible and can meet all kinds of requests from different users. Information of evaluate result is converted into graphic by using visualization technology. The implement of visual communication between people and data or among people makes the evaluation model have user friendly interface and feasible.
The following work has been innovatively completed in this paper.
1.3D scanning data analysis of women's torso based on data mining
After 3D scanning data of women's torso is analyzed by means of descriptive algorithm of data mining, two conclusions can be drawn. In storey dimension, women's torso is divided into 50 layers evenly spaced from bottom to top. Layer 10, 20,29,31,34,36 are both the maximum value of first order difference and the minimum value of second order difference. Layer 10,20,31 are severally located near the hip line, waist line and bust line. Layer 29,31 are respectively situated near the under bust line and upper bust line. Layer 34 is seated between upper bust line and bust line. The six layers are the key layers that present surface features of women's torso. In fan dimension, women's torso is equally divided into 8 portions by three via origin planes that the frontal direction contour, the lateral direction contour and the rotation 45°direction contour respectively seat. The Angle degree of two nearby planes is 45°. The three group fans that drop shadow to plane of projection are the key fans that present surface features of women's torso. After 3D scanning data of women's torso is analyzed by means of forecasting algorithm of data mining, the following conclusion can be drawn. There is a multi valued dependency relationship between key points and other points on each layer. The value of a non-key point can be predicted according to the value of the three nearby key points by using linear regression data mining algorithm. The error is in the acceptable range.
2. Building standards system of data acquisition, transmission and storage of 3D body
The method of 3D body data acquisition based on the conclusion of 3D body data mining is studied which is by means of three photographs that are captured at designated angle. The acquisition and distortion of body image has been adjusted in order that data accuracy can fulfill the employ of clothing industry. The body contours is extracted from images that are captured at designated angle and have been corrected the distortion. The characteristic point and key points are extracted from body contours, based on which the other points are calculated.3D body data are acquired through the above steps. Accessory appliances such as table turning of angular dimensioning have been fabricated. The data acquisition system of 3D body that can provide point cloud data has been developed. A feasible solution of representation and transmission of 3D body data is put forward by the application of XML technology according to association rules that have been extracted by data mining. XML documents are interpreted and verified in the environment of Visual Studio 2005. Database model of 3D body is built and implemented in MS SQL Sever 2005 database.
3. Building individual virtual mannequin that is used for evaluation model of clothes fit
Individual virtual mannequin that is used for evaluation model of clothes fit is built. The parameters to build individual virtual mannequin are provided in four levels that are size level, characteristic point level, key point level and cloud point level in the order from simple to complex. The parameters of different levels can be combined. The third level named key point level is adopted in this paper. The scheme of acquiring parameter for individual virtual mannequin is built through the use of Web Service technology, which can realize interconnection of loosely coupling distributed component. Both various data structure stored on different operating systems and different hardware platforms of various merchants and diverse program interface of individual body data are availably integrated, which share the individual body data of clients in all merchants.3D coordinate data of point cloud of women's torso are created according to key point data. Surfaces model of body based on Cubic B-spline technology is built after point cloud data being grid processed. In addition, many lines of reference such as neck line, bust line, waist line, hip line, front center line, back center line, side seam are built on the model, which is preparing for evaluation model of clothes fit.
4. Building three levels evaluation model of clothes fit
In the architecture of three levels evaluation model of clothes fit, the first level named evaluation model based on size designation is the minimum level, in which algorithm is easy, the number of data required is small, operation time is less, which is appropriate for evaluation of clothes that are loose in style and have great flexibility in fabric. The second level is evaluation model based on reference lines, which is based on the first level. Only if evaluate result of the first level is provisional accreditation and user selects the option of next level, evaluation model based on reference lines is carried out. The third level is evaluation model based on surface of body, which is based on the second level. Only if evaluate result of the second level is provisional accreditation and user selects the option of next level, evaluation model based on surface of body is carried out. The third level is the maximum level, which needs many detailed data of both body and clothes. Operation time is more. The third level is appropriate for evaluation of clothes that are fitted in style and have small flexibility in fabric. The three levels evaluation model of clothes fit has the applicable scope to be broad, which can meet the requirements of the development E-commerce, fashion online marketing and Internet of Things. The evaluation model is highly efficient and the three sub-models are also the progressive process in which clothes that don't even match can be weeded out, thus the three sub-models are not completing execution which not only save the resources and calculation, but also shorten the time of evaluation. During the procedure of implementation, sub-model is optional for user, which is flexible and can meet all kinds of requests from different users. Information of evaluate result is converted into graphic by using visualization technology. The implement of visual communication between people and data or among people makes the evaluation model have user friendly interface and feasible.
引文
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