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基于L_1范数的形状快速匹配算法 被引量:1
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作者 王江辉 吴小俊 《计算机应用研究》 CSCD 北大核心 2019年第1期264-267,27,共5页
针对内距离形状上下文(inner-distance shape context,IDSC)和轮廓点分布直方图(contours points distribution histogram,CPDH)在形状相似性度量中直方图匹配耗时长、工程应用性不佳的问题,提出了一种用EMD-L_1测量轮廓特征直方图距离... 针对内距离形状上下文(inner-distance shape context,IDSC)和轮廓点分布直方图(contours points distribution histogram,CPDH)在形状相似性度量中直方图匹配耗时长、工程应用性不佳的问题,提出了一种用EMD-L_1测量轮廓特征直方图距离的方法。EMD-L_1在原始EMD(earth mover’s distance)的基础上融合了L_1范数,通过替换地面距离计算方法,减少了目标函数的变量,加快了直方图匹配的速度,能够快速实现形状匹配并保持较好的检索性能。对形状数据集进行仿真实验的结果证明,该方法能够有效地进行数据集的形状识别和检索,并且在MNIST数据集下的匹配速度优于其他算法。 展开更多
关键词 内距离形状上下文 轮廓点分布直方图 地球移动距离 l1范数 形状检索
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Seizure detection using earth movers' distance and SVM in intracranial EEG
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作者 王芸 吴琦 +2 位作者 周卫东 袁莎莎 袁琦 《Journal of Measurement Science and Instrumentation》 CAS 2014年第3期94-102,共9页
Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram (iEEG... Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram (iEEG). The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers' distance (EMD), with support vector machine (SVM) for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG. 展开更多
关键词 electroencephalograph (EEG)signals earth movers' distance emd emd-l1 support vector machine(SVM) wavelet decomposition seizure detection
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