A conformal multi-resolution time-domain( CMRTD) method is presented for modeling curved objects. The effective dielectric constant and area weighting are used to derive the update equations of CMRTD. The backward sca...A conformal multi-resolution time-domain( CMRTD) method is presented for modeling curved objects. The effective dielectric constant and area weighting are used to derive the update equations of CMRTD. The backward scattering bistatic radar cross sections( RCS) of the dielectric cylinder and ellipsoid are used to validate the proposed method. The results show that the proposed conformal method is more accurate to deal with the complex curved objects in electromagnetic simulations.展开更多
In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentati...In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentation. First the MV field is temporally and spatially normalized, and then accumulated by an iterative backward projection to enhance salient motions and alleviate noisy MVs. The accumulated MV field is then segmented into motion-homogenous regions using a modified statistical region growing approach. Finally, moving object regions are extracted in turn based on minimization of the joint prediction error using the estimated motion models of two region sets containing the candidate object region and other remaining regions, respectively. Experimental results on several H.264 compressed video sequences demonstrate good segmentation performance.展开更多
三支决策将不确定样本划分至边界域进行延迟决策,但需基于损失函数确定阈值,以划分边界域,然而,损失函数通常需要先验知识,具有一定的主观性,因此对边界域划分能力不足。针对这种问题,构建一种多目标三支决策边界域求解方法,从而更好地...三支决策将不确定样本划分至边界域进行延迟决策,但需基于损失函数确定阈值,以划分边界域,然而,损失函数通常需要先验知识,具有一定的主观性,因此对边界域划分能力不足。针对这种问题,构建一种多目标三支决策边界域求解方法,从而更好地划分边界域,提升分类性能。采用贝叶斯规则获取样本的条件概率;设定3个目标,包括降低边界域的不确定性、缩小边界域的大小以及减小整个决策区域的错误分类率,通过融入熵权法的TOPSIS(technique for order preference by similarity to an ideal solution)方法求取最优阈值,该方法采用熵权法计算这3个目标所占的权重,得到最优阈值,获得边界域,进行延迟决策;结合不同分类器对边界域进行分类。通过UCI数据集进行对比实验,根据分类准确率和F1值,表明该方法学习到的阈值能合理地划分边界域,建立的模型能取得更好的分类性能。展开更多
基金Supported by the National Natural Science Foundation of China(61172024)the Funding of Jiangsu Innovation Program for Graduate Education and the Fundamental Research Funds for the Central Universities(CXZZ12-0156)
文摘A conformal multi-resolution time-domain( CMRTD) method is presented for modeling curved objects. The effective dielectric constant and area weighting are used to derive the update equations of CMRTD. The backward scattering bistatic radar cross sections( RCS) of the dielectric cylinder and ellipsoid are used to validate the proposed method. The results show that the proposed conformal method is more accurate to deal with the complex curved objects in electromagnetic simulations.
基金Project supported by the National Natural Science Foundation of China （Grant No.60572127）, the Development Foundation of Shanghai Municipal Commission of Education （Grant No.05AZ43）, and the Shanghai Leading Academic Discipline Project （Grant No.T0102）
文摘In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentation. First the MV field is temporally and spatially normalized, and then accumulated by an iterative backward projection to enhance salient motions and alleviate noisy MVs. The accumulated MV field is then segmented into motion-homogenous regions using a modified statistical region growing approach. Finally, moving object regions are extracted in turn based on minimization of the joint prediction error using the estimated motion models of two region sets containing the candidate object region and other remaining regions, respectively. Experimental results on several H.264 compressed video sequences demonstrate good segmentation performance.
文摘三支决策将不确定样本划分至边界域进行延迟决策,但需基于损失函数确定阈值,以划分边界域,然而,损失函数通常需要先验知识,具有一定的主观性,因此对边界域划分能力不足。针对这种问题,构建一种多目标三支决策边界域求解方法,从而更好地划分边界域,提升分类性能。采用贝叶斯规则获取样本的条件概率;设定3个目标,包括降低边界域的不确定性、缩小边界域的大小以及减小整个决策区域的错误分类率,通过融入熵权法的TOPSIS(technique for order preference by similarity to an ideal solution)方法求取最优阈值,该方法采用熵权法计算这3个目标所占的权重,得到最优阈值,获得边界域,进行延迟决策;结合不同分类器对边界域进行分类。通过UCI数据集进行对比实验,根据分类准确率和F1值,表明该方法学习到的阈值能合理地划分边界域,建立的模型能取得更好的分类性能。
