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local feature recognition
相关语句
  局部特征识别
     Research on local feature recognition techniques for concurrent design.
     面向并行设计的局部特征识别技术研究
短句来源
     Local feature recognition for radiation images based on SVMs and DWT
     基于SVM和DWT的辐射图像局部特征识别方法
短句来源
     The method of synchronous design feature and machining feature modeling based on local feature recognition is presented in Chapter 3. With the evolution of design process, this method incrementally creates machining feature model and realizes synchronous design feature and machining feature modeling based on local feature recognition.
     第三章介绍基于局部特征识别的设计特征和加工特征同步建模方法。 该方法随着设计过程的演进,基于局部特征识别,逐步地生成加工特征模型,实现了设计特征与加工特征的同步建模,并且通过使用动态关联表记录实体模型中拓扑元素的变化信息,使得需识别区域的确定方便、快捷。
短句来源
     In the concurrent design, the design feature model and the machining feature model share the same solid model of a part through a history graph of each face's identity, while design feature changes are converted into corresponding machining features automatically and incrementally by local feature recognition.
     在并行设计中,设计特征模型和加工特征模型通过面名历史图共享零件的实体模型,设计特征的变动通过局部特征识别自动地转换为相应的加工特征.
短句来源
     A SVMs (support vector machines) based radiation image local feature recognition algorithm was designed and developed.
     针对辐射图像的特点,设计并开发了一种支持向量机(SVM)和基于离散小波变换(DWT)的局部特征识别方法。
短句来源
  局部特征识别的
     Feature Validity Maintaining Approach Based on Local Feature Recognition
     基于局部特征识别的特征有效性维护方法
短句来源
     The method of synchronous design feature and machining feature modeling based on local feature recognition is presented in Chapter 3. With the evolution of design process, this method incrementally creates machining feature model and realizes synchronous design feature and machining feature modeling based on local feature recognition.
     第三章介绍基于局部特征识别的设计特征和加工特征同步建模方法。 该方法随着设计过程的演进,基于局部特征识别,逐步地生成加工特征模型,实现了设计特征与加工特征的同步建模,并且通过使用动态关联表记录实体模型中拓扑元素的变化信息,使得需识别区域的确定方便、快捷。
短句来源
     In this paper, a representation for feature validity condition based on the extended attributed adjacency graph is proposed, and a novel approach is particularly proposed to feature validity maintaining using local feature recognition technique.
     在对特征有效性条件进行深入分析的基础上,提出了一个基于扩展属性邻接图(extended attributed adjacency graph,简称EAAG)的特征有效性表示方法,特别是提出了基于局部特征识别的特征有效性维护新方法.
短句来源
     To facilitate the incremental conversion from design feature model to machining feature model in concurrent design, an approach to local feature recognition is presented.
     为支持在并行设计过程中设计特征模型到加工特征模型的逐步转换,提出了局部特征识别的方法.
短句来源
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Lacking of feature validity maintaining function has become a serious problem with existing feature modeling systems. In this paper, a representation for feature validity condition based on the extended attributed adjacency graph is proposed, and a novel approach is particularly proposed to feature validity maintaining using local feature recognition technique. The approach can not only automatically detect if the feature validity is destroyed, but also accurately determine the reason why the feature...

Lacking of feature validity maintaining function has become a serious problem with existing feature modeling systems. In this paper, a representation for feature validity condition based on the extended attributed adjacency graph is proposed, and a novel approach is particularly proposed to feature validity maintaining using local feature recognition technique. The approach can not only automatically detect if the feature validity is destroyed, but also accurately determine the reason why the feature becomes invalid and the state of the destroyed feature. Furthermore, the approach can automatically renovate the feature model based on the user's intention.

缺乏特征模型的有效性维护功能已经成为目前特征造型系统存在的一个严重而亟待解决的问题.在对特征有效性条件进行深入分析的基础上,提出了一个基于扩展属性邻接图(extended attributed adjacency graph,简称EAAG)的特征有效性表示方法,特别是提出了基于局部特征识别的特征有效性维护新方法.该方法不仅能够自动判别特征的有效性是否被破坏,而且能确定导致特征无效的原因和遭破坏后特征的状态,从而能够根据用户的意图自动维持特征模型的有效性.

To facilitate the incremental conversion from design feature model to machining feature model in concurrent design, an approach to local feature recognition is presented. In the concurrent design, the design feature model and the machining feature model share the same solid model of a part through a history graph of each face's identity, while design feature changes are converted into corresponding machining features automatically and incrementally by local feature recognition....

To facilitate the incremental conversion from design feature model to machining feature model in concurrent design, an approach to local feature recognition is presented. In the concurrent design, the design feature model and the machining feature model share the same solid model of a part through a history graph of each face's identity, while design feature changes are converted into corresponding machining features automatically and incrementally by local feature recognition. The method of local feature recognition is derived from formerly proposed Minimal- Condition Sub- Graph (MCSG) method, and to some extent, enhances the original MCSG method. In contrast to the global feature recognition method in which a part is processed as a whole, the local feature recognition method restricts the recognition area in a local area of a part, and recognizes machining features by matching boundary patterns in the bounded local area. Experiments show that great efficiency can be obtained since the recognition area is kept within bounds of the local area where there are design feature changes.

为支持在并行设计过程中设计特征模型到加工特征模型的逐步转换,提出了局部特征识别的方法.在并行设计中,设计特征模型和加工特征模型通过面名历史图共享零件的实体模型,设计特征的变动通过局部特征识别自动地转换为相应的加工特征.局部特征识别是由基于最小条件子图特征识别方法改进来的,它以零件的局部区域为识别对象,通过搜索匹配局部区域构成的边界模式,识别出该局部区域中所包含的加工特征.局部特征识别方法的特点是只对设计中发生变动的区域进行识别.

A SVMs (support vector machines) based radiation image local feature recognition algorithm was designed and developed. Using a set of 4000 simulated images, we achieved at least 93. 4% detec tion rate and 0. 8% false positive rate. The empolyment of wavelet, in particluar discrete wavelet trans form (DWT), adds multi-resolution support to the algorithm and incresases total recognition perfor mance. DWT decomposes a radiation image into subbands and stresses features of different scales in each...

A SVMs (support vector machines) based radiation image local feature recognition algorithm was designed and developed. Using a set of 4000 simulated images, we achieved at least 93. 4% detec tion rate and 0. 8% false positive rate. The empolyment of wavelet, in particluar discrete wavelet trans form (DWT), adds multi-resolution support to the algorithm and incresases total recognition perfor mance. DWT decomposes a radiation image into subbands and stresses features of different scales in each subband. Because of the standout generalization capbility of SVMs, using subbands directly as in put becomes possible and produces compellent results. In this paper, different kernel functions are also compared to each other. Experiments show that Gaussian Raial Basis Function (RBF) kernel overtakes the others in our application.

针对辐射图像的特点,设计并开发了一种支持向量机(SVM)和基于离散小波变换(DWT)的局部特征识别方法。使用支持向量机解决了辐射图像局部特征提取的困难和分类器对样本数目的要求。而小波的应用使得该算法能够支持多分辨率的特征提取,并提高了总体识别效率。还对比了两种常见的核函数,实验结果表明高斯径向基函数能够取得比较好的分类效果。

 
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