三维FMF的HFCM水声数据分割
针对三维水声数据背景复杂、受噪声干扰严重等特点，提出一种结合三维FMF的HFCM水声数据分割算法，以提高水声数据分割的精度和效率。该算法首先选取三维滤波窗口，利用最大熵阈值法计算出模糊阈值；再结合半高斯模糊隶属度函数对水声数据进行模糊中值滤波；最后采用HFCM算法对滤波后的数据进行分割。对两组不同的三维水声数据进行分割处理的结果表明，该算法能够有效地降低噪声干扰，分割效果要优于未滤波的HFCM以及均衡FMF的HFCM分割算法，并且在分割效率上要明显优于传统的模糊C-均值算法。...
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| Published in | 计算机应用研究 Vol. 34; no. 10; pp. 3005 - 3009 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
杭州电子科技大学通信工程学院模式识别与信息安全实验室,杭州,310018
2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-3695 |
| DOI | 10.3969/j.issn.1001-3695.2017.10.028 |
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| Summary: | 针对三维水声数据背景复杂、受噪声干扰严重等特点，提出一种结合三维FMF的HFCM水声数据分割算法，以提高水声数据分割的精度和效率。该算法首先选取三维滤波窗口，利用最大熵阈值法计算出模糊阈值；再结合半高斯模糊隶属度函数对水声数据进行模糊中值滤波；最后采用HFCM算法对滤波后的数据进行分割。对两组不同的三维水声数据进行分割处理的结果表明，该算法能够有效地降低噪声干扰，分割效果要优于未滤波的HFCM以及均衡FMF的HFCM分割算法，并且在分割效率上要明显优于传统的模糊C-均值算法。 |
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| Bibliography: | fuzzy median filter（FMF） ; histogram fuzzy C-means（HFCM） ; data segmentation; maximum entropy threshold method; semi-Gaussian fuzzy membership function 51-1196/TP The 3D underwater acoustic data usually contains complex background and serious noise pollution. With regard to these features, this paper presented an underwater acoustic data segmentation algorithm via histogram fuzzy C-means （HFCM） with 3D fuzzy median filter （FMF） to improve segmentation accuracy and efficiency. This method first selected a 3D filter window, and calculated the fuzzy threshold by the maximum entropy threshold method in the filter window. Then it filtered the underwater acoustic data by the FMF with the semi-Gaussian fuzzy membership function. Finally, it adopted the HFCM segmentation algorithm to segment the filtered data. The results of segmentation of two different groups of 3D underwater acoustic data show that the effect of the segmentation algorithm is better than the unfiltered HFCM segmentation algorithm and the ttFCM segmen |
| ISSN: | 1001-3695 |
| DOI: | 10.3969/j.issn.1001-3695.2017.10.028 |