首页> 外文会议>IEEE Conference on Big Data and Analytics >Analysis and modeling of alcohol intoxication from IR images based on multiregional image segmentation and correlation with breath analysis
【24h】

Analysis and modeling of alcohol intoxication from IR images based on multiregional image segmentation and correlation with breath analysis

机译:基于多舰图像分割与呼吸分析相关性的IR图像中酒精中毒的分析与建模

获取原文

摘要

Alcohol intoxication is an important procedure which is related to all social areas. There are commonly used standards like are the blood analysis or breath analysis. Although such methods are commonly used and give satisfactory results, there are also certain drawbacks. For instance it is direct contact and awareness of the tested person. One challenging direction of the alcohol assessment is the temperature effect whilst drinking, thus temperature variations may be reliable indicators of the current alcohol state. The paper deals with a comparative analysis of three multiregional segmentation methods with target of building of a mathematical model reflecting the facial areas well reflecting the dynamical process of the alcohol drinking. By such modelling we can make a predictor of the alcohol state based on the facial temperature effect. We have specified two significant features: nose and forehead areas where the temperature variations are well observable. Eventually, we have done a verification analysis between individual dynamical models and breath analysis on the base of the Pearson correlation coefficient. Correlation gives relatively strong dependence, when we consider a fact that some persons have stronger inclination to the alcohol which may negatively influence the IR records and the segmentation results as well.
机译:酒精中毒是与所有社会领域有关的重要程序。常用标准似乎是血液分析或呼吸分析。虽然这些方法通常使用并提供令人满意的结果,但也存在某些缺点。例如,它是直接接触和对测试人员的认识。酒精评估的一个具有挑战性的方向是饮用时的温度效应,因此温度变化可能是当前醇状态的可靠指示剂。本文涉及三个多洲分割方法的比较分析,其中三个多洲分割方法具有反映了反映酒精饮用动力过程的面积区域的数学模型的建设。通过这种建模,我们可以基于面部温度效应来制造醇状态的预测因子。我们已经指定了两个重要特征:温度变化的鼻子和前额区域是良好的观察。最终,我们在Pearson相关系数的基础上进行了个体动态模型和呼气分析的验证分析。当我们认为某些人对酒精可能产生负面影响和细分结果的事实,我们认为有些人的相关性提供了相对强烈的依赖。

著录项

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