Shen Xilin, Dietlein Charles R, Grossman Erich, Popovic Zoya, Meyer François G
Department of Radiology, Yale University, New Haven, CT 06519, USA.
IEEE Trans Image Process. 2008 Dec;17(12):2465-75. doi: 10.1109/TIP.2008.2006662.
Terahertz imaging makes it possible to acquire images of objects concealed underneath clothing by measuring the radiometric temperatures of different objects on a human subject. The goal of this work is to automatically detect and segment concealed objects in broadband 0.1-1 THz images. Due to the inherent physical properties of passive terahertz imaging and associated hardware, images have poor contrast and low signal to noise ratio. Standard segmentation algorithms are unable to segment or detect concealed objects. Our approach relies on two stages. First, we remove the noise from the image using the anisotropic diffusion algorithm. We then detect the boundaries of the concealed objects. We use a mixture of Gaussian densities to model the distribution of the temperature inside the image. We then evolve curves along the isocontours of the image to identify the concealed objects. We have compared our approach with two state-of-the-art segmentation methods. Both methods fail to identify the concealed objects, while our method accurately detected the objects. In addition, our approach was more accurate than a state-of-the-art supervised image segmentation algorithm that required that the concealed objects be already identified. Our approach is completely unsupervised and could work in real-time on dedicated hardware.
太赫兹成像通过测量人体上不同物体的辐射温度，使得获取隐藏在衣物下的物体图像成为可能。这项工作的目标是在0.1 - 1太赫兹的宽带图像中自动检测并分割隐藏物体。由于被动太赫兹成像及相关硬件的固有物理特性，图像对比度差且信噪比低。标准分割算法无法分割或检测出隐藏物体。我们的方法分两个阶段。首先，我们使用各向异性扩散算法去除图像噪声。然后，我们检测隐藏物体的边界。我们使用高斯密度混合模型来模拟图像内部温度的分布。接着，我们沿着图像的等值线演化曲线以识别隐藏物体。我们已将我们的方法与两种最先进的分割方法进行了比较。这两种方法都未能识别出隐藏物体，而我们的方法准确地检测出了物体。此外，我们的方法比一种最先进的监督图像分割算法更准确，后者要求隐藏物体已被识别。我们的方法是完全无监督的，并且可以在专用硬件上实时运行。
