EAGER: Combining Sketching and Computer Vision Techniques in Cultural Heritage Applications
EAGER: Combining Sketching and Computer Vision Techniques in Cultural Heritage Applications
批准号:
0949911
负责人:
Holly Rushmeier
金额:
$10.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-09-01 至 2011-02-28
中文摘要
计算机图形学已成功地应用于文化遗产的广泛领域,无论是在专题专家的研究中,还是在为公众教育准备展览中,都证明了这一点。每年,世界各地都会启动数百个基于计算机图形的文化遗产项目。然而,数字材料的使用仍然要求很高,需要技术专家和学科专家。该研究项目将影响用于向公众传达历史和文化信息的展览的发展。准备这样的展品将更容易为主题的专家,它将有可能创造更多的参与信息的交互式显示本研究的目标是改变创作和使用数字对象在通信的方法。该研究解决了产生令人信服的3D显示的遗产遗址,其中大型档案摄影文件的问题。最近的创新,在草图为基础的建模,特别是?精神画布?仅依赖于3D笔划而不是完整实体的框架将与计算机视觉技术一起沿着被利用。Mental Canvas框架将允许用户注释,以促进图像特征检测和束估计的最新改进的应用,从而从存档图像计算3D点。注释是必需的,因为当前的方法往往失败时,所有的相机特性是未知的,照片不是从旨在促进三维重建的意见。该系统的输出将是一个3D模型,可以很容易地由一个普通用户导航。然后,该系统将能够自然地在空间中导航,浏览实体模型、档案图像和文本数据。耶鲁美术馆正在准备一个来自杜拉欧洲遗址的文物和图像展览,作为这项研究的试验平台。
英文摘要
Computer graphics has been successfully used in a wide range of cultural heritage applications.It has been proven both in the context of study by subject specialists and in the preparation of exhibits for education of the general public. Each year hundreds more computer graphics based cultural heritage projects are initiated around he world. However, the use of digital material remains demanding and requires technologists as well as subject specialists. This research project will impact the development of exhibitions used to communicate historical and cultural information to the general public. Preparing such exhibits will be easier for subject specialists, and it will be possible to create more engaging interactive displays of informationThe goal of this research is to change the methods for both authoring and using digital objects in communication. The research addresses the problem of generating compelling 3D displays of heritage sites, where large archival photographic documentation is available. Recent innovations in sketch-based modeling, in particular the ?Mental Canvas? framework that relies only on 3D strokes rather than full solids, will be exploited along with computer vision techniques. The Mental Canvas framework will allow user annotation in order to facilitate the application of recent improvements in image feature detection and bundle estimation to compute 3D points from archival images. Annotation is required since current methods often fail when all camera characteristics are unknown and photographs were not taken from views designed to facilitate 3D reconstruction. The output of the proposed system will be a 3D model that can be easily navigated by a casual user. The system will then be able to naturally navigate through space and browse solid models, archival imagery and textual data. An exhibit of artifacts and imagery from the Dura Europos site that is being prepared by the Yale Art Gallery is serving as the testbed for the research.
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