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成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关于视觉世界的知识的能力。然而，要实现这一目标，需要共同致力于开发新的形状和图像分析方法，并设计协作用户界面，使多个领域的专家和插图画家能够将他们的专业知识聚集在一起。协作算法渲染引擎(CARE)将是一个开源工具，用于提取和合并只有在特定照明条件、特定波长或特定成像模式下才能使用的视觉细节。通过专注于最小的用户工作量、跨站点协作可视化设计以及集成的存档和过程历史(来源)跟踪，CARE工具被专门设计来消除广泛采用用于视觉分析和交流的数字工具的现有障碍。作为该项目的一部分，研究人员正在开发建立在现有技术基础上的新的图像分析技术，例如反射变换成像(RTI)和使用具有法线的图像的非照片真实感渲染(RGBN NPR)，这些技术已经在文化遗产界引起了极大的兴趣。研究包括以下方法：(1)分析图像集合，将其分解成每个像素上的颜色、方向和材质的“地图”；(2)分别对部分或全部地图执行任意顺序或图像处理操作的组合；以及(3)将几个地图组合成最终插图。整个过程是由(4)为交互响应而设计的用户界面所驱动的，该用户界面包括能够实现协作插图设计的特殊功能。该项目涉及大学研究小组与非营利性公司之间的密切合作，该研究小组负责新技术的开发，而非营利性公司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着良好的记录。这种联合开发将确保基础技术将立即在该领域产生重大影响：文化遗产学者和科学家将能够为科学论文和教科书制作高质量、易理解的插图，并控制选择性地强调、对比、关注和抽象，比手工制作这些数字的成本更低，灵活性更大。艺术史这一主题还提供了一个独特的机会，可以激发那些通常不会学习计算机科学课程的学生的兴趣，扩大学生接触计算机工具和能力的班级。

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.