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X射线图像集中的对象分离

Object Separation In X-Ray Image Sets
课程网址: http://videolectures.net/cvpr2010_heitz_osxr/  
主讲教师: Geremy Heitz
开课单位: 奎尔安全系统公司
开课时间: 2010-07-19
课程语种: 英语
中文简介:
在自然图像的分割中,大多数算法都是关于遮挡的概念。然而,在x射线图像中,这种假设被违反,因为x射线光子穿透了材料。在本文中,我们介绍了SATISφ,这是一种利用对数空间中可加性的特性来分离一组x射线图像中的对象的方法,其中像素的对数衰减是相应x射线通过的所有对象的对数衰减的总和。我们的方法从略微不同的角度利用相同景点的多个投影视图,以产生对景物中物体的衰减特性的准确估计。这些属性可用于识别这些对象的材料成分,因此对于自动威胁检测等应用程序至关重要。我们在一组收集的X射线扫描上评估了SATISφ,表明它优于标准的图像分割方法,并减少了材料估计的误差。
课程简介: In the segmentation of natural images, most algorithms rely on the concept of occlusion. In x-ray images, however, this assumption is violated, since x-ray photons penetrate most materials. In this paper, we introduce SATISφ, a method for separating objects in a set of x-ray images using the property of additivity in log space, where the logattenuation at a pixel is the sum of the log-attenuations of all objects that the corresponding x-ray passes through. Our method leverages multiple projection views of the same scene from slightly different angles to produce an accurate estimate of attenuation properties of objects in the scene. These properties can be used to identify the material composition of these objects, and are therefore crucial for applications like automatic threat detection. We evaluate SATISφ on a set of collected x-ray scans, showing that it outperforms a standard image segmentation approach and reduces the error of material estimation.
关 键 词: 算法; 遮挡; 对象分离
课程来源: 视频讲座网
最后编审: 2019-03-12:cwx
阅读次数: 90