EECS Department, University of Toledo, MS 308, 2801W. Bancroft Street, Toledo, OH 43606, USA.
J Hazard Mater. 2011 Feb 28;186(2-3):1254-62. doi: 10.1016/j.jhazmat.2010.11.129. Epub 2010 Dec 7.
This paper, for the first time, applies the support vector machines (SVMs) paradigm to identify the optimal segmentation algorithm for physical characterization of particulate matter. Size of the particles is an essential component of physical characterization as larger particles get filtered through nose and throat while smaller particles have detrimental effect on human health. Typical particulate characterization processes involve image reading, preprocessing, segmentation, feature extraction, and representation. Of these various steps, knowledge based selection of optimal image segmentation algorithm (from existing segmentation algorithms) is the key for accurately analyzing the captured images of fine particulate matter. Motivated by the emerging machine-learning concepts, we present a new framework for automating the selection of optimal image segmentation algorithm employing SVMs trained and validated with image feature data. Results show that the SVM method accurately predicts the best segmentation algorithm. As well, an image processing algorithm based on Sobel edge detection is developed and illustrated.
本文首次应用支持向量机（SVM）范例来确定用于颗粒物物理特性描述的最佳分割算法。颗粒大小是物理特性描述的一个重要组成部分，因为较大的颗粒会被鼻子和喉咙过滤掉，而较小的颗粒则会对人体健康造成有害影响。典型的颗粒物特性描述过程包括图像读取、预处理、分割、特征提取和表示。在这些不同的步骤中，基于知识的最佳图像分割算法（从现有分割算法中选择）的选择是准确分析细颗粒物捕获图像的关键。受新兴机器学习概念的启发，我们提出了一种新的框架，使用经过图像特征数据训练和验证的 SVM 来自动选择最佳图像分割算法。结果表明，SVM 方法能够准确地预测最佳分割算法。此外，还开发并说明了一种基于 Sobel 边缘检测的图像处理算法。
