一种新型进化方法用于计算机辅助颅面叠加中的自动颅面叠加
A Novel Evolutionary Method for Automated Skull-Face Overlay in Computer-Aided Craniofacial Superimposition
AI总结：
本文提出Lilium方法，通过进化算法优化3D锥形表示，提升颅面叠加的准确性和鲁棒性，克服软组织厚度差异带来的不确定性。
摘要：
颅面叠加是一种法医技术，通过比较死后颅骨与生前面部照片识别骨骼残骸。其中关键步骤是颅面叠加（SFO），涉及将3D颅骨模型与2D面部图像对齐，通常通过颅面部标志点对应引导。然而，软组织厚度的个体差异会降低叠加精度。本文提出Lilium，一种自动进化方法，通过3D锥形表示显式建模软组织变化，并利用微分进化算法优化参数。方法通过约束条件确保解剖、形态和摄影的合理性，包括标志点匹配、相机参数一致性、头部姿态对齐、颅骨位于面部边界内以及区域平行性。这种对传统法医从业者方法的模拟使Lilium在准确性和鲁棒性上优于现有最佳方法。
英文摘要：
Craniofacial Superimposition is a forensic technique for identifying skeletal remains by comparing a post-mortem skull with ante-mortem facial photographs. A critical step in this process is Skull-Face Overlay (SFO). This stage involves aligning a 3D skull model with a 2D facial image, typically guided by cranial and facial landmarks' correspondence. However, its accuracy is undermined by individual variability in soft-tissue thickness, introducing significant uncertainty into the overlay. This paper introduces Lilium, an automated evolutionary method to enhance the accuracy and robustness of SFO. Lilium explicitly models soft-tissue variability using a 3D cone-based representation whose parameters are optimized via a Differential Evolution algorithm. The method enforces anatomical, morphological, and photographic plausibility through a combination of constraints: landmark matching, camera parameter consistency, head pose alignment, skull containment within facial boundaries, and region parallelism. This emulation of the usual forensic practitioners' approach leads Lilium to outperform the state-of-the-art method in terms of both accuracy and robustness.
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