• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Multi-view, broadband, acoustic classification of marine animals.
【24h】

Multi-view, broadband, acoustic classification of marine animals.

机译：海洋动物的多视图，宽带，声学分类。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Acoustical methods provide rapid, non-invasive, and synoptic tools for studying marine ecosystems. Despite the dramatic advances in this technology during the past three decades, there is presently a large disparity between the demand for quantitative information about marine animals and the capability of acoustic systems to deliver this information. A primary reason for this disparity is the strong dependence of acoustic scatter from marine animals on their size, shape, in situ orientation, and taxa. In a typical setting, these parameters are unknown, and are difficult to determine using existing acoustic methods. To mitigate this problem, a multi-view, broadband approach to marine animal classification and size estimation is investigated in this thesis.;Initially, zooplankton classification was investigated for two ecologically important taxa: copepods and euphausiids. Numerical simulations compared physics-based feature transformations, Nearest Neighbor (NN), and Multi-Layer Perceptron (MLP) classifiers. Results indicate that combining frequency-correlation features with a MLP yields an accurate (> 90 % correct) classification algorithm. Based on these promising results, a laboratory system was developed to recorded multi-view, broadband scatter from live, individual copepods and mysids. Results using frequency correlation features indicate that these features yield very good separation between classes with non-overlapping standard deviations computed from eight individuals per class.;Next, sound scatter data from live, individual fish were used to develop several kernel-machine-based multi-view fusion algorithms. Performance was quantitatively compared as a function of the number of available views, feature spaces, and classification problem type. A collaborative fusion algorithm performs better than the others without requiring any assumption about view geometry, the number of views, or the type of features.;Finally, multi-view fish size and orientation estimation was investigated under three different approaches. Results indicate that classification-based size estimation can be effective with a limited aperture and limited number of views. Model-based and image-reconstruction-based estimation show very good performance with full aperture data.;This thesis demonstrates that the multi-view, broadband approach offers significant advantages for marine animal classification, sizing, and orientation estimation.
机译：声学方法为研究海洋生态系统提供了快速，非侵入性和天气的工具。尽管在过去的三十年中该技术取得了巨大的进步，但目前对海洋动物定量信息的需求与声学系统传递此信息的能力之间存在很大差距。造成这种差异的主要原因是海洋动物的声音散射强烈依赖于它们的大小，形状，原位方向和分类群。在典型设置中，这些参数是未知的，并且难以使用现有的声学方法确定。为缓解这一问题，本文研究了一种多视角，宽带的海洋动物分类和大小估计方法。最初，对浮游动物的分类进行了生态学上重要的两个类群的研究：co足类和e类。数值模拟比较了基于物理的特征转换，最近邻（NN）和多层感知器（MLP）分类器。结果表明，将频率相关特征与MLP相结合可得出准确的（正确率> 90％）分类算法。基于这些有希望的结果，开发了一个实验室系统来记录来自活的，个体individual足类动物和类鸦片的多视点，宽带散射。使用频率相关特征的结果表明，这些特征可以在类别之间实现很好的分离，并且每个类别有八个个体计算出不重叠的标准偏差。;接下来，使用来自活鱼个体的声音散射数据来开发几种基于核机器的多视图融合算法。根据可用视图，功能空间和分类问题类型的数量对性能进行了定量比较。协作融合算法的性能优于其他融合算法，无需对视图几何形状，视图数量或特征类型进行任何假设。最后，在三种不同方法下研究了多视图鱼的大小和方向估计。结果表明，基于分类的大小估计可以在有限的孔径和有限数量的视图下有效。基于模型和基于图像重建的估计在全孔径数据下显示出非常好的性能。;本文证明，多视图，宽带方法为海洋动物的分类，大小和方向估计提供了显着的优势。

著录项

  • 作者

    Roberts, Paul L. D.;

  • 作者单位

    University of California, San Diego.;

  • 授予单位 University of California, San Diego.;
  • 学科 Biology Oceanography.;Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 184 p.
  • 总页数 184
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 海洋生物;无线电电子学、电信技术;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号