基于最大二维熵的被动式太赫兹安检目标分割

Concealed object segmentation based on maximum two-dimensional entropy for passive terahertz security

摘要:

针对被动式太赫兹安检的检测需求，提出一种基于最大二维熵的隐蔽目标分割方法。该方法设计了一组适用于被动式太赫兹图像的滤波器组，实现噪声过滤和图像增强；设计了一种待检区域自生成的算法，实现对重点检测区域的自动覆盖；同时，引入二维熵的概念，实现对待检测区域内的隐蔽目标的轮廓分割。在0.2 THz频段的被动成像下开展了评估和对比实验，实验表明本文方法具有较好的分割效果和实时性能。

Abstract:

A method of the concealed object segmentation based on maximum two-dimensional entropy for passive terahertz security is proposed. The method firstly employs a filter bank to reduce image noise. A self-generated detection region algorithm is designed, which can automatically cover the key detection area. The concept of two-dimensional entropy is introduced to implement the concealed object segmentation. Evaluation and comparison experiments are conducted in 0.2 THz band passive images, demonstrating that the method has a good segmentation performance and real-time performance. It may have an important application in the automatic detection for terahertz security.