Enhancing Undergraduate Computer Science Curriculum through Image Computations: Proof-of-Concept

通过图像计算加强本科计算机科学课程：概念验证

• 批准号: 9980832
• 负责人: Sudeep Sarkar
• 金额: $ 7.52万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-01-01 至 2001-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9980832&HistoricalAwards=false
• 关键词: Enhancing; Undergraduate; Computer; Science; Curriculum

Computer Science (31)This proof-of-concept project is developing educational materials to integrate research progress in image related computation (computer vision, image processing and image analysis) into the undergraduate computer science and engineering core curriculum. Recent years have experienced an explosion of image gathering modalities along with a resultant increase in the demand for expertise in image related computation. Considerable progress has also been made in fields such as image processing, image analysis, and computer vision. It is no longer sufficient to address this need for image expertise through one upper level elective course in computer vision. The need to incorporate image related knowledge units in core undergraduate courses such as data structures, introductory programming courses, automata theory, computer ethics, databases, networks, coding theory, and computer algorithms, preferably without major change in the content of these courses is essential. The materials for these knowledge units are being developed so that they can be used by instructors who are not necessarily computer vision specialists. An added advantage of using image related knowledge units in various courses is that, because of their inherent visual nature and large sizes, images offer an excellent medium for the better understanding of underlying core computer science concepts.As deliverables, the project is developing specific image-related knowledge units and instructional support materials, such as class handouts, transparencies, lecture materials, software, and descriptions of active learning contexts. Dissemination is being accomplished through the development of a textbook and freely available website. The accumulated experience and evaluation results are being presented at major computer science education conferences, workshops, and through journal papers.

计算机科学(31)这个概念验证项目正在开发教育材料，将与图像相关的计算(计算机视觉、图像处理和图像分析)的研究进展整合到本科计算机科学和工程核心课程中。近年来经历了图像采集模式的爆炸性增长以及由此导致对图像相关计算专业知识的需求的增加。在图像处理、图像分析和计算机视觉等领域也取得了长足的进步。仅通过一门计算机视觉的高级选修课来满足对图像专业知识的需求已不再足够。有必要将与图像相关的知识单元纳入核心本科课程，如数据结构、编程入门课程、自动机理论、计算机伦理学、数据库、网络、编码理论和计算机算法，这些课程的内容最好不发生重大变化。这些知识单元的材料正在开发中，以便供不一定是计算机视觉专家的教员使用。在各种课程中使用与图像相关的知识单元的另一个好处是，由于其固有的可视性质和大小，图像为更好地理解基本的核心计算机科学概念提供了一个极好的媒介。作为交付成果，该项目正在开发特定的与图像相关的知识单元和教学支持材料，如课堂讲义、透明胶片、授课材料、软件和对积极学习环境的描述。目前正在通过编写教科书和免费提供的网站进行传播。积累的经验和评估结果将在主要的计算机科学教育会议、研讨会和期刊论文中公布。