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）该概念验证项目正在开发教育材料，以将研究进度与图像相关计算（计算机视觉，图像处理和图像分析）的研究进度整合到本科计算机科学和工程核心课程中。近年来，图像收集方式发生了爆炸，以及对图像相关计算中对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。通过计算机视觉中的一门高级选修课程来满足图像专业知识的需求不足以满足这种需求。需要将与图像相关的知识单位合并到核心本科课程中，例如数据结构，入门编程课程，自动机理论，计算机伦理，数据库，网络，编码理论和计算机算法，最好是这些课程内容的重大变化是必不可少的。这些知识单元的材料正在开发中，因此不一定是计算机视觉专家的讲师可以使用它们。在各种课程中使用与图像相关的知识单元的额外优势是，由于其固有的视觉性质和大尺寸，图像为更好地理解基础核心计算机科学概念提供了一种极好的媒介。作为可交付成果，该项目正在开发特定的图像 - 相关的知识单位和教学支持材料，例如班级讲义，透明胶片，讲座材料，软件以及主动学习环境的描述。通过开发教科书和免费提供的网站来实现传播。 积累的经验和评估结果正在主要的计算机科学教育会议，研讨会和期刊论文中提出。