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Adaptive image quality improvement with Bayesian classification for in-line monitoring.

机译：贝叶斯分类的自适应图像质量改进用于在线监测。

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摘要

Development of an automated method for classifying digital images using a combination of image quality modification and Bayesian classification is the subject of this thesis. The specific example is classification of images obtained by monitoring molten plastic in an extruder. These images were to be classified into two groups: the "with particle" (WP) group which showed contaminant particles and the "without particle" (WO) group which did not. Previous work effected the classification using only an adaptive Bayesian model. This work combines adaptive image quality modification with the adaptive Bayesian model. The first objective was to develop an off-line automated method for determining how to modify each individual raw image to obtain the quality required for improved classification results. This was done in a very novel way by defining image quality in terms of probability using a Bayesian classification model. The Nelder Mead Simplex method was then used to optimize the quality. The result was a "Reference Image Database" which was used as a basis for accomplishing the second objective. The second objective was to develop an in-line method for modifying the quality of new images to improve classification over that which could be obtained previously. Case Based Reasoning used the Reference Image Database to locate reference images similar to each new image. The database supplied instructions on how to modify the new image to obtain a better quality image. Experimental verification of the method used a variety of images from the extruder monitor including images purposefully produced to be of wide diversity. Image quality modification was made adaptive by adding new images to the Reference Image Database. When combined with adaptive classification previously employed, error rates decreased from about 10% to less than 1% for most images. For one unusually difficult set of images that exhibited very low local contrast of particles in the image against their background it was necessary to split the Reference Image Database into two parts on the basis of a critical value for local contrast. The end result of this work is a very powerful, flexible and general method for improving classification of digital images that utilizes both image quality modification and classification modeling.
机译：本文提出了一种结合图像质量修正和贝叶斯分类的数字图像自动分类方法。具体示例是通过监控挤出机中熔融塑料获得的图像分类。将这些图像分为两类：显示有污染物颗粒的“有颗粒”（WP）组和未显示污染物的“无颗粒”（WO）组。先前的工作仅使用自适应贝叶斯模型来实现分类。这项工作将自适应图像质量修改与自适应贝叶斯模型结合在一起。第一个目标是开发一种离线自动方法，用于确定如何修改每个单独的原始图像以获得改善分类结果所需的质量。这是通过使用贝叶斯分类模型以概率定义图像质量以一种非常新颖的方式完成的。然后使用Nelder Mead Simplex方法优化质量。结果是一个“参考图像数据库”，该数据库被用作实现第二个目标的基础。第二个目标是开发一种在线方法，用于修改新图像的质量，以改进与以前可获得的图像相比的分类。基于案例的推理使用参考图像数据库来定位与每个新图像相似的参考图像。数据库提供了有关如何修改新图像以获得更好质量图像的说明。该方法的实验验证使用了来自挤出机监控器的各种图像，包括有意产生的多样性很大的图像。通过将新图像添加到参考图像数据库中，可以自适应地修改图像质量。当与先前采用的自适应分类相结合时，大多数图像的错误率从大约10％降至小于1％。对于一组异常困难的图像，这些图像相对于背景在图像中的粒子局部对比度非常低，有必要根据局部对比度的临界值将参考图像数据库分为两部分。这项工作的最终结果是利用图像质量修改和分类建模来改善数字图像分类的一种非常强大，灵活且通用的方法。

著录项

  • 作者

    Yan, Shuo.;

  • 作者单位

    University of Toronto (Canada).;

  • 授予单位 University of Toronto (Canada).;
  • 学科 Engineering Chemical.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 258 p.
  • 总页数 258
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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