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The ALISA Component Module: General shape classification for digital radiographic images.

机译：ALISA组件模块：数字射线照相图像的常规形状分类。

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摘要

Accurate and robust object classification is an unsolved problem in the field of digital radiographic image (DRI) analysis. The problem is confounded by variations in objects' scale, rotation, translation, point-of-view, partial obscuration, and noise. In addition, DRIs present a unique set of challenges, since they reveal the internal structures and features of objects rather than the surface features revealed in most other types of images. The objective of this research is to design, implement, and test a general classifier for DRIs that is robust in the presence of occlusions and noise. To achieve this objective, the proposed ALISA Component Module will use a generalized two-tier analysis token to extract feature vectors associated with the pixel at the center of the analysis token. The feature vectors are accumulated in histograms during training, and classification is performed by comparing feature vectors generated from test images to trained histograms. Formal experiments conducted with the Component Module operating on both canonical shapes and real-world applications using DRIs demonstrated robust classification performance. The Experiments have also demonstrated that the Component Module can learn to classify components of objects from small training sets, as well as effectively classify similar components independent of their position and some variation in their orientations. The Component Module is also robust to uniform and non-uniform occlusions, and noise.
机译：准确和鲁棒的对象分类是数字放射线图像（DRI）分析领域中尚未解决的问题。问题是由于对象的比例，旋转，平移，视点，部分遮挡和噪声的变化而引起的。此外，DRI提出了一系列独特的挑战，因为它们揭示了对象的内部结构和特征，而不是大多数其他类型的图像中揭示的表面特征。这项研究的目的是设计，实施和测试DRI的通用分类器，该分类器在存在遮挡和噪声的情况下非常可靠。为了实现此目标，建议的ALISA组件模块将使用广义的两层分析令牌来提取与分析令牌中心处的像素关联的特征向量。在训练期间将特征向量累积在直方图中，并且通过将从测试图像生成的特征向量与训练后的直方图进行比较来执行分类。使用DRI对在规范形状和实际应用程序上运行的组件模块进行的正式实验证明了强大的分类性能。实验还证明，组件模块可以学习从小型训练集中对对象的组件进行分类，并且可以有效地对类似组件进行分类，而与它们的位置和方向的变化无关。组件模块对于均匀和非均匀的遮挡以及噪声也很强大。

著录项

  • 作者

    Oertel, Carsten K.;

  • 作者单位

    The George Washington University.;

  • 授予单位 The George Washington University.;
  • 学科 Artificial Intelligence.;Computer Science.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 276 p.
  • 总页数 276
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;自动化技术、计算机技术;
  • 关键词

  • 入库时间 2022-08-17 11:37:51

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