[1]陈蕾,赵正旭,陶智. 3DS文件特征提取器的设计与实现[J].计算机技术与发展,2017,27(10):161-154.
　CHEN Lei,ZHAO Zheng-xu,TAO Zhi. Design and Implementation of 3DS File Feature Extractor[J].,2017,27(10):161-154.
点击复制

 3DS文件特征提取器的设计与实现()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年10期
页码:
161-154
栏目:
应用开发研究
出版日期:
2017-10-10

文章信息/Info

Title:
 Design and Implementation of 3DS File Feature Extractor
文章编号:
1673-629X（2017)10-0161-04
作者:
 陈蕾; 赵正旭; 陶智
 石家庄铁道大学 信息科学与技术学院
Author(s):
 CHEN Lei; ZHAO Zheng-xu; TAO Zhi
关键词:
 ; 规范化; 3DS ; 块ID ; 特征提取
Keywords:
 ; standardization; 3DS; chunk ID; feature extraction
分类号:
TP301
文献标志码:
A
摘要:
 深空探测可视化系统中存在着数以万计的三维模型,这些模型杂乱无章,将其规范化管理的方法之一就是分类编码.为此,选取天体模型作为3DS(3DStudio)格式的文件,并在进行分类编码时选择文件的十个重要特征作为分类点.为了科学地提取3DS文件特征并为后期分类编码提供准确的文件信息,设计并提出了一个3DS文件特征提取器.该提取器通过输入3DS文件解析其块结构,从中提取材质、贴图、顶点、对象数等十个目标特征信息,并根据块ID识别重要块并以txt格式输出.为验证所提出提取器的有效性和可行性,以土卫三作为验证实例,通过提取结果来检验所提出文件特征提取器的科学性、规范性和适用性.验证实验结果表明,所设计的提取器提取到的模型特征信息与实际模型属性信息相吻合,能够实现对目标模型的指定信息提取,且提取数据真实有效,为提取模型文件特征提供了一个科学、规范的方法,对于天体模型的规范化管理具有一定的参考价值.
Abstract:
 There are massive 3D models in visiualization system of outer space missions,all of which are disorganized in a mess and need to be classified with classification coding method for standardized management. Therefore 3DS (3DStudio) files format is chosen to de-scribe the celestial models and 10 key features of these 3DS files are selected as classification characterastics during classifying and cod-ing. In order to extract the features of the 3DS files and to provide accurate file information for subsequent classifying and coding,a 3DS file feature extractor is proposed and designed which parses the block structure of the 3DS file and extracts the 10 specified features inclu-ding material,texture,vertex,object number and so on,to identify important blocks and output file in txt format according to block ID. In order to verify its effectiveness and applicability in engineering applications,the Tethys model is chosen as an example,with which the ex-tracted results are analyzed to prove its scientificity,normalization and applicability. The experiment results show that feature information extracted from the proposed extractor is consistent with the actual attribute information and it has implemented the specific information ex-traction of object model with real and effective data,which has supplied a scientific and normative method for features extraction of model files with a reference value for normative management of the celestial models.

相似文献/References:

[1]李高仕 刘先锋 黄海燕.XML文档规范化算法研究[J].计算机技术与发展,2008,(07):48.
　LI Gao-shi,LIU Xian-feng,HUANG Hai-yan.Study on Normalization Algorithms for XML Documents[J].,2008,(10):48.
[2]陶勇 丁维明.数据库中规范化与反规范化设计的比较与分析[J].计算机技术与发展,2006,(04):107.
　TAO Yong,DING Wei-ming.Comparison and Analysis of Normalization and Denormalization Design in Database[J].,2006,(10):107.
[3]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(10):1.
[4]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(10):5.
[5]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(10):13.
[6]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(10):21.
[7]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(10):25.
[8]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(10):29.
[9]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(10):34.
[10]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(10):38.

更新日期/Last Update: 2017-11-24