CT-ER: Privacy Algorithms for Human Imaging Systems

CT-ER：人体成像系统的隐私算法

• 批准号: 0716657
• 负责人: Yang Cai
• 金额: --
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2008-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0716657&HistoricalAwards=false
• 关键词: CT; ER; Privacy; Algorithms; Human

The rapidly growing demand of 3D holographic imaging systems has created a significant interest in many disciplines. However, these scanned images reveal human body details and raise privacy concerns. In this project, the team develops: 1) a virtual test bed for privacy-aware imaging. Similar to the "phantoms" widely used in medical studies, the team intends to develop a device-independent test database that can simulate the output of a 3D human body scanner for a broader research community. 2) The team develops robust and fast algorithms for segmenting human bodies and detecting private body parts. Many machine learning algorithms are coordinate-dependent and limited by the training data space. Existing algorithms only work within small bounding boxes that do not warrant an acceptable performance. In this project, the team uses innovative Analogia Graph and Relative Template Matching algorithms for speed and accuracy. 3) The team develops the algorithm for detecting anomalous objects on human body using curvature measurement and spatial density filtering models. Finally, 4) the team develops a method for assessing the visual privacy algorithms based on detection accuracy, privacy and aesthetic measurements. The anticipated results will have broader impacts on privacy-aware security in airports and other large infrastructures. It can benefit custom-fit products that are designed from personal 3D scanning data. It can be used in reconstruction of ancient artifacts in digital archeology. In addition, it can be applied to medical diagnoses, such as virtual colonoscopy. Finally, this project can benefit multidisciplinary education of human-centric systems.

3D 全息成像系统需求的快速增长引起了许多学科的浓厚兴趣。然而，这些扫描图像会泄露人体细节并引发隐私问题。在这个项目中，团队开发了：1）用于隐私感知成像的虚拟测试台。与医学研究中广泛使用的“体模”类似，该团队打算开发一个独立于设备的测试数据库，可以为更广泛的研究社区模拟 3D 人体扫描仪的输出。 2) 该团队开发了强大而快速的算法来分割人体和检测私人身体部位。许多机器学习算法都依赖于坐标并受到训练数据空间的限制。现有算法仅适用于小边界框，无法保证可接受的性能。在这个项目中，团队使用创新的 Analogia Graph 和相对模板匹配算法来提高速度和准确性。 3）团队开发了利用曲率测量和空间密度过滤模型检测人体异常物体的算法。最后，4）团队开发了一种基于检测精度、隐私和美观测量来评估视觉隐私算法的方法。预期结果将对机场和其他大型基础设施的隐私安全安全产生更广泛的影响。它可以使根据个人 3D 扫描数据设计的定制产品受益。它可用于数字考古中古代文物的重建。此外，它还可以应用于医疗诊断，例如虚拟结肠镜检查。最后，该项目可以有益于以人为中心的系统的多学科教育。