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conventional region
相关语句
  普通的区域
     The result of conventional region growing algorithm is sensitive to the division order and there is no evaluate mechanism for seeking the optimal merges. The semantic growing method integrate the priori information into the image segmentation process,but it does not estimate properties of newly merged regions, it attempts to obtain the overall optimum value of objective function from partial data.
     普通的区域增长的图像分割算法对于分割的顺序过于敏感,且不具备普适性的最优评价机制,而语义增长方法虽然将先验知识结合到图像分割中,但没有估计归并到的新区域性质,试图从部分数据得到全局的最优值。
短句来源
  经典区域
     To solve the difficulty of feature selection and region growing rule existing in conventional region growing image segmentation algorithm, a multiple objects parallel growing algorithm based on support vector machines was proposed.
     为克服经典区域增长算法中生长规则以及特征选取的困难,提出了基于支持向量机的多目标并行区域增长图像分割算法。
短句来源
     To overcome the difficult of threshold initialization and precise object segmentation existing in conventional region growing algorithm,a accurate image segmentation method combined support vector learning parallel region growing and active contour model was proposed.
     为克服经典区域增长算法门限设置困难和图像分割精度不高的问题,提出了基于支持向量机学习的区域增长与活动轮廓模型结合的高精度图像分割算法。
短句来源
     A multiple-object parallel growing algorithm based on two-layer support vector machines with rejection feature is proposed to solve the difficulty in feature selection and region growing rule in the conventional region growing image segmentation algorithm.
     针对经典区域增长算法中生长规则以及特征选取困难的问题,提出基于可拒识双层支持向量机模型的多目标并行区域增长图像分割算法。
短句来源
     To overcome the difficulty of threshold selection and region growing rule existing in conventional region growing image segmentation algorithm,a multiple regions parallel growing algorithm based on Gaussian mixture model(GMM) is proposed.
     为克服经典区域增长算法中生长规则以及特征选取的困难,提出了基于高斯混合模型的多区域并行区域增长图像分割算法。
短句来源
     To overcome the difficulty of manual threshold selection and slow speed of conventional region growing image segmentation algorithm, a region evolution image segmentation algorithm is proposed.
     为克服经典区域增长算法中门限选择困难、分割稳定性不高与串行处理速度慢的不足,提出了基于区域进化的快速区域增长图像分割算法。
  “conventional region”译为未确定词的双语例句
     In northern China, 1.67×10~8 hm~2 of temperate grassland, almost covering 41% of the national grassland area, are presented as zonal distribution from northeast to southwest mainly distributed in northern and western China, This range from northwest desert to southeast agricultural district forms a natural green ecological shelter and wildlife habitat, and also conventional region of animal husbandry in northwest China.
     我国北方温带草原约有1.67亿hm~2约占全国草地面积的41%,集中分布在西部、北部呈东北-西南带状分布,横亘于西北大漠和东南部农区之间,是我国西北部地区的一道天然绿色生态屏障和野生动物的栖息地,也是我国传统畜牧业的生产基地。
短句来源
     Experimental results show that this algorithm is feasible and valid, and it performs better than conventional region growing algorithm.
     仿真实验获得了较好的分割效果,表明提出的算法是合理可行的。
短句来源
     Experimental results show that this algorithm is feasible and it performs better than conventional region growing algorithm.
     仿真实验获得了较好的分割效果,表明该提出的算法是合理可行的。
短句来源
     Experimental results show that this algorithm is feasible and it performs better than conventional region growing algorithm.
     仿真实验获得了较好的分割效果,实验结果表明,提出的算法是合理可行的。
短句来源
     Experimental results show that this algorithm is feasible,and it performs better than conventional region growing algorithm.
     仿真实验获得了较好的分割效果,表明所提出的算法是合理可行的。
短句来源
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  conventional region
Applying conventional region-of-interest (ROI) analysis algorithms to such 3-D spectra is problematic due to spectral interferences in the low-resolution spectra.
      
In this study we evaluated posterior cingulate perfusion as a relative value using statistical parametric maps (SPMs) and as an absolute value using conventional region of interest (ROI) settings.
      
First, we apply the same procedure as the conventional region-based intelligent scissors to create a weighted graph.
      
Conventional region growing, a threshold-based method with user-defined starting points.
      
As the literature and the survey results indicate, these do have visualisation advantages over the conventional region quadtree.
      


To solve the difficulty of feature selection and region growing rule existing in conventional region growing image segmentation algorithm, a multiple objects parallel growing algorithm based on support vector machines was proposed. Initial seeds in each object were selected by interactive manual operation. At the same time all the pixels near the seed point belonged to the same object. Then the support vector classifier was trained by the training data collected. The region growing rules were established...

To solve the difficulty of feature selection and region growing rule existing in conventional region growing image segmentation algorithm, a multiple objects parallel growing algorithm based on support vector machines was proposed. Initial seeds in each object were selected by interactive manual operation. At the same time all the pixels near the seed point belonged to the same object. Then the support vector classifier was trained by the training data collected. The region growing rules were established by making use of the support vector classifier. In order to make results independent of processing order and the initial growing seeds, multiple object parallel growing was employed. Experimental results show that this algorithm is feasible and valid, and it performs better than conventional region growing algorithm.

为克服经典区域增长算法中生长规则以及特征选取的困难,提出了基于支持向量机的多目标并行区域增长图像分割算法。首先交互选择多个不同区域的种子点(假设邻近种子点的一小部分区域属于同一目标区域,不同种子点邻域分别属于不同目标区域),利用其邻域构造训练样本;然后利用这些已知的训练样本训练支持向量分类器;在区域增长过程中,利用训练所得的支持向量模型建立增长规则。为了避免初始种子点位置选择对算法性能的影响,采用了多区域并行竞争增长策略。仿真实验获得了较好的分割效果,表明提出的算法是合理可行的。

The result of conventional region growing algorithm is sensitive to the division order and there is no evaluate mechanism for seeking the optimal merges. The semantic growing method integrate the priori information into the image segmentation process,but it does not estimate properties of newly merged regions, it attempts to obtain the overall optimum value of objective function from partial data.The genetic algorithm be used in the image division, and the priori information also be considered in...

The result of conventional region growing algorithm is sensitive to the division order and there is no evaluate mechanism for seeking the optimal merges. The semantic growing method integrate the priori information into the image segmentation process,but it does not estimate properties of newly merged regions, it attempts to obtain the overall optimum value of objective function from partial data.The genetic algorithm be used in the image division, and the priori information also be considered in the construct of the objective function.this method fully utilize the genetic algorithms characteristic, thus avoid the partially optimum problem occurs effectively in the process of image segmentation.

普通的区域增长的图像分割算法对于分割的顺序过于敏感,且不具备普适性的最优评价机制,而语义增长方法虽然将先验知识结合到图像分割中,但没有估计归并到的新区域性质,试图从部分数据得到全局的最优值。将遗传算法用于图像分割,并将先验知识结合到目标函数中,充分利用了遗传算法的特点,从而有效地解决了图像分割中陷于局部最优的问题。

To overcome the difficult of threshold initialization and precise object segmentation existing in conventional region growing algorithm,a accurate image segmentation method combined support vector learning parallel region growing and active contour model was proposed.The blocks belong to the object region or belong to the background region were interactive selected.The support vector classifier was trained by the training data collected in the first step.In region growing processing...

To overcome the difficult of threshold initialization and precise object segmentation existing in conventional region growing algorithm,a accurate image segmentation method combined support vector learning parallel region growing and active contour model was proposed.The blocks belong to the object region or belong to the background region were interactive selected.The support vector classifier was trained by the training data collected in the first step.In region growing processing step,the support vector classifier(SVC) with maximum soft margin was used for establishing region growing rules,so as to obtain the initial object contour.In order to achieve accurate segmentation results,the active contour model was exploited to further segment to got accurate boundary. Experimental results show that this algorithm is feasible and it performs better than conventional region growing algorithm.

为克服经典区域增长算法门限设置困难和图像分割精度不高的问题,提出了基于支持向量机学习的区域增长与活动轮廓模型结合的高精度图像分割算法。首先交互式选择属于目标区域的子块和背景区域的子块形成支持向量机的训练样本;并利用这些已知的训练样本训练支持向量分类器。在目标与背景的并行竞争增长过程中,利用训练好的支持向量分类器(SVC)进行分类判决,得到目标对象的初始轮廓。为提高分割对象的精度,采用活动轮廓模型获得准确的边缘。仿真实验获得了较好的分割效果,表明该提出的算法是合理可行的。

 
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