基于模糊c均值聚类算法的车辆检测

Ridvan Saraçoglu, N. Nemati
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引用次数: 2

摘要

车辆检测与识别是交通控制与管理领域中非常重要的功能。一般来说,为了更接近这个功能,需要对大数据集和区域特征进行研究。目的是为这些数据找到最合适的模型。此外,为数据准备的模型旨在识别图像上的因素。换句话说,它旨在将因子分配到正确的类别并区分它们。用这种方法对图像进行分类。本文提出了一种采用模糊c均值算法进行图像分割、支持向量机进行图像分类的车辆识别系统。这些方法的时效性是它们最重要的特性。结果表明,所选方法的应用是成功有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vehicle Detection Using Fuzzy C-Means Clustering Algorithm
Vehicle detection and identification are very important functions in the field of traffic control and management. Generally, a study should be conducted on big data sets and area characteristics to get closer to this function. The aim is to find the most appropriate model for these data. Also, the model that is prepared for the data aims to recognize the factors on the image. In other words, it aims to assign factors to the right classes and differentiate them. A classification of the image is made in that way. In this study, a vehicle identification system, in which Fuzzy C-Means Algorithm is used for image segmentation and the Support Vector Machine is used for image classification, is presented. The currentness of these methods is their most important property. The obtained results show that the selected methods are applied successfully and effectively.
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