Geometric Analysis for Classification and Reassembly of Broken Bones

用于断骨分类和重组的几何分析

• 批准号: 1816917
• 负责人: Peter Olver
• 金额: $ 41.81万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816917&HistoricalAwards=false
• 关键词: Geometric; Analysis; Classification; Reassembly; Broken

The reassembly of broken objects is a fundamental issue in many real-world applications. The goal of this project is to apply modern machine learning and geometric tools to the problems of classification and automated reassembly of broken bone fragments from an archaeological context, to determine agents of breakage (e.g., carnivore, hominin, etc.,), improve taxonomic identifications, and better understand site formation processes through spatial analysis of refits. The anthropological implications are expected to impact the study of early human origins and dispersal, prehistory, culture, predator avoidance, and social organization -- hunting, scavenging, food provisioning, etc. The project relies on samples and data generated both locally by known agents and through ongoing field work in Dmanisi, Georgia; if successful, these methods can be applied to archaeological sites from all times around the world. The potential impact of success is underscored by the April, 2017 announcement that, based on the geometry of broken mastodon bones, humans settled the Americas 100,000 years earlier than the standard estimates of 30-40,000 years ago, although this claim remains highly controversial in the field. Accurate and precise methods of determining the agent of breakage have not yet been completely worked out by archaeologists, and hence a reassessment of their bone analysis would be of supreme interest. Besides zooarchaeological applications, potential areas of significant impact include computer-aided and virtual reassembly of other archaeological objects (pottery, statuary, lithics and tools, etc.), paleontology (dinosaurs and other fossils), art restoration, and computer-assisted surgery, where the mathematical techniques can aid the surgeon to both plan and undertake an operation while minimizing the invasiveness and impact on the patient. Other areas where these techniques have already had some impact include the reassembly of jigsaw puzzles, shredded documents, and whole histological sections from digitized tissue fragments, as well as the diagnosis of cancer in breast tumors and the distinguishing of moles from melanomas. Graduate and undergraduate students participate in the research.The project seeks to adapt and extend known geometric methods, data analysis, and numerical schemes, particularly those based on continuous and discrete invariant signatures, to the problem of analyzing and reconstructing broken solid objects, with a particular emphasis on bone fragments. Notable success in the automatic reassembly of non-pictorial jigsaw puzzles and broken surfaces, e.g., egg shells, using differential invariant signatures, suggests that one of the key goals of the project, the three-dimensional solid object reconstruction problem, is attainable. In addition, new (to anthropological field work) geometric tools of surface geometry -- principal curvatures, torsion and curvature of three-dimensional break curves, histogram and other discrete integral invariants -- are applied to analyze breakage geometry, starting with controlled samples of ungulate (elk, cow, and goat) bones that have been broken by humans using stone tools and by animals (hyenas in the Milwaukee County Zoo and the Irvine Zoo in Chippewa Falls), in preparation for the eventual analysis of field samples from Dmanisi and possibly other sites around the world, such as Olduvai Gorge in Tanzania and The Cradle of Humankind in South Africa. This analysis is used to develop machine learning algorithms, both fully supervised and semi-supervised, for classifying bone fragments based on agent of breakage. We map the bone fragments into feature spaces via computation of histograms of geometric invariants, and train a machine learning algorithm, such as k-nearest neighbors or support vector machine classifiers, to distinguish between different agents of breakage. The controlled samples of ungulate bones broken by humans are used as training data, and field data from the Dmanisi site are used as testing data. Graduate and undergraduate students participate in the research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在许多现实世界的应用程序中，破碎对象的重新组装是一个基本问题。 该项目的目标是将现代机器学习和几何工具应用于考古背景下断裂骨碎片的分类和自动重组问题，以确定断裂因素（例如，食肉动物、古人类等），改进分类鉴定，并通过空间分析的refits更好地了解网站的形成过程。 人类学的影响预计将影响早期人类起源和扩散、史前史、文化、避免捕食者和社会组织-狩猎、拾荒、食物供应等的研究。如果成功，这些方法可以应用于世界各地的考古遗址。 2017年4月的一项声明强调了成功的潜在影响，该声明基于破碎乳齿象骨骼的几何形状，人类在美洲定居的时间比标准估计的30- 40，000年前早100，000年，尽管这一说法在该领域仍然存在很大争议。 考古学家们还没有完全研究出确定断裂原因的准确而精确的方法，因此，重新评估他们的骨骼分析将是最有意义的。 除了动物考古学应用之外，具有重大影响的潜在领域包括计算机辅助和虚拟重组其他考古对象（陶器，雕像，石器和工具等），古生物学（恐龙和其他化石），艺术品修复和计算机辅助手术，其中数学技术可以帮助外科医生计划和进行手术，同时最大限度地减少对患者的侵入性和影响。 这些技术已经产生了一定影响的其他领域包括拼图游戏的重组，切碎的文件，以及从数字化组织碎片中的整个组织切片，以及乳腺肿瘤中癌症的诊断和痣与黑色素瘤的区分。 研究生和本科生参与了这项研究。该项目旨在调整和扩展已知的几何方法，数据分析和数值方案，特别是那些基于连续和离散不变签名的方法，以分析和重建破碎的固体物体，特别是骨骼碎片。 在自动重新组装非图案拼图和破碎表面方面取得了显著的成功，例如，蛋壳，使用微分不变签名，表明该项目的关键目标之一，三维固体物体重建问题，是可以实现的。 此外，新（适用于人类学田野调查）从有蹄类动物的控制样本入手，应用曲面几何学的几何工具--三维断裂曲线的主曲率、挠率和曲率、直方图和其他离散积分不变量分析断裂几何（麋鹿、牛和山羊）被人类用石器和动物折断的骨头（密尔沃基县动物园和齐佩瓦福尔斯的欧文动物园的鬣狗），为最终分析来自德马尼西和可能世界各地其他地点的野外样本做准备，如坦桑尼亚的奥杜威峡谷和南非的人类摇篮。 该分析用于开发完全监督和半监督的机器学习算法，用于基于断裂代理对骨碎片进行分类。 我们通过计算几何不变量的直方图将骨碎片映射到特征空间中，并训练机器学习算法，例如k-最近邻或支持向量机分类器，以区分不同的断裂剂。 被人类破坏的有蹄类动物骨骼的受控样本被用作训练数据，来自Dmanisi站点的现场数据被用作测试数据。 该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Computation of Circular Area and Spherical Volume Invariants via Boundary Integrals

通过边界积分计算圆形面积和球体体积不变量

• DOI: 10.1137/19m1260803
• 发表时间: 2020
• 期刊: SIAM Journal on Imaging Sciences
• 影响因子: 2.1
• 作者: O'Neill, Riley C.;Angulo-Uman͂a, Pedro;Calder, Jeff;Hessburg, Bo;Olver, Peter J.;Shakiban, Chehrzad;Yezzi-Woodley, Katrina
• 通讯作者: Yezzi-Woodley, Katrina

The virtual goniometer: demonstrating a new method for measuring angles on archaeological materials using fragmentary bone

虚拟测角仪：展示一种使用碎片骨测量考古材料角度的新方法

• DOI: 10.1007/s12520-021-01335-y
• 发表时间: 2021
• 期刊: Archaeological and Anthropological Sciences
• 影响因子: 2.2
• 作者: Yezzi-Woodley, Katrina;Calder, Jeff;Olver, Peter J.;Cody, Paige;Huffstutler, Thomas;Terwilliger, Alexander;Melton, J. Anne;Tappen, Martha;Coil, Reed;Tostevin, Gilbert
• 通讯作者: Tostevin, Gilbert