CDI-Type I: Automated Documentation and Illustration of Material Culture through the Collaborative Algorithmic Rendering Engine (CARE)

计算机将来自多种成像模式的视觉信息统一为可理解的插图的能力将彻底改变科学家、工程师和人文学者获取和交流视觉世界知识的能力。然而，实现这一目标，将需要共同关注开发新的形状和图像分析方法，并设计协作用户界面，允许多个领域专家和插图画家汇集他们的专业知识。协作算法渲染引擎（CARE）将是一个开源工具，用于提取和合并仅在特定照明条件、特定波长或特定成像模式下可用的视觉细节。通过关注最小的用户工作量，跨站点协作可视化设计，以及集成归档和过程历史（来源）跟踪，CARE工具专门设计用于消除广泛采用数字工具进行可视化分析和沟通的现有障碍。作为该项目的一部分，研究人员正在开发新的图像分析技术，这些技术建立在现有技术的基础上，如反射变换成像（RTI）和使用法线图像的非真实感渲染（RGBN NPR），这些技术已经在文化遗产界引起了极大的兴趣。研究方法包括：(1)对图像集合进行分析，将其分解为每个像素的颜色、方向和材质的“地图”；（二）对部分或者全部地图分别进行任意顺序或者组合的图像处理操作；(3)将几张地图组合成最终的插图。整个过程是由(4)为交互响应设计的用户界面驱动的，其中包括使协作插图设计成为可能的特殊功能。该项目涉及一个负责新技术开发的大学研究小组和一家在与博物馆和考古遗址合作部署新型成像和计算摄影系统方面有良好记录的非营利公司之间的密切合作。这种联合开发将确保基础技术将在该领域产生直接的高影响：文化遗产学者和科学家将能够为科学论文和教科书生成高质量，可理解的插图，具有可控制的重点，对比，注意和抽象，成本更低，灵活性更大，比手工生成这些数字。艺术史的主题也提供了一个独特的机会来激发学生的兴趣，他们通常不会参加计算机科学课程，扩大学生接触到计算机的工具和能力。

The ability of computers to unify visual information from multiple imagingmodes into comprehensible illustrations will revolutionize the ability ofscientists, engineers, and humanities scholars to gain and communicateknowledge about the visual world. Achieving this goal, however, willrequire a joint focus on developing novel shape and image analysis methods,and designing collaborative user interfaces that allow multiple domainexperts and illustrators to bring together their expertise. TheCollaborative Algorithmic Rendering Engine (CARE) will be an open-source toolfor extracting and merging visual details available only under certainlighting conditions, certain wavelengths, or certain imaging modalities. By focusing on minimal user effort, cross-site collaborative visualizationdesign, and integrated archiving and process history (provenance) tracking,the CARE tool is specifically designed to remove existing obstacles towidespread adoption of digital tools for visual analysis and communication.As part of the project, investigators are developing novel image analysistechniques that build upon existing technologies such as ReflectanceTransformation Imaging (RTI) and non-photorealistic rendering using imageswith normals (RGBN NPR), which have already received enormous interestwithin the cultural heritage community. The research includes methods for:(1) analyzing the collection of images to decompose them into "maps" ofcolor, orientation, and material at each pixel; (2) performing an arbitrarysequence or combination of image-processing operations on some or all ofthe maps separately; and (3) combining several maps into the finalillustration. The whole process is driven by (4) a user interface designedfor interactive response and including special features that enablecollaborative illustration design.The project involves a close collaboration between a university-based researchgroup, responsible for development of new technologies, and a non-profitcompany with a demonstrated track record of working with museums andarchaeological sites to deploy novel imaging and computational photographysystems. This joint development will ensure that the underlyingtechnologies will have immediate high impact in the field: culturalheritage scholars and scientists will be able to generate high-quality,comprehensible illustrations for scientific papers and textbooks, with controlover selective emphasis, contrast, attention, and abstraction, at lowercost and greater flexibility than generating such figures by hand. The subjectmatter of art history also offers the unique opportunity to stimulate theinterest of students who would not normally take courses in computerscience, broadening the class of students exposed to the tools and capabilitiesof computing.