CT-ER: Privacy Algorithms for Human Imaging Systems

3D全息成像系统快速增长的需求已经在许多学科中产生了显著的兴趣。然而，这些扫描图像揭示了人体的细节，引起了人们对隐私的担忧。在这个项目中，团队开发了：1)一个用于隐私感知成像的虚拟测试平台。与医学研究中广泛使用的“幻影”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团体模拟3D人体扫描仪的输出。2)团队开发了稳健快速的人体分割和检测私密部位的算法。许多机器学习算法依赖于坐标，并且受训练数据空间的限制。现有的算法只能在小的边界框内工作，不能保证可接受的性能。在这个项目中，团队使用创新的类比图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滤波模型检测人体异常物体的算法。最后，该团队开发了一种基于检测精度、隐私和美学测量来评估视觉隐私算法的方法。预期的结果将对机场和其他大型基础设施的隐私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。它可以用于数字考古中古代文物的重建。此外，它还可以应用于医学诊断，如虚拟结肠镜检查。最后，本项目可促进以人为本系统的多学科教育。

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.