College education has historically, for the most part, focused on the products of scientific investigation (i.e., the facts). Many students today acquire their learning of a particular field through structured courses which restrict the students to passive watching rather than active doing. The overall goal of this project, sponsored by the National Science Foundation, (#0088086 ) is to increase the effectiveness of student learning experience by integrating the results of recent and ongoing research on Computer Vision into the Computer Science and Engineering Curriculum at the University of Nevada, Reno (UNR). Our philosophy is that the integration of teaching with research should happen at all levels, leading to a comprehensive instructional program, offering systematic and constant research experiences for as many students as possible. This project seeks to immerse students into research through systematic and structured activities starting at the sophomore year and continuing until graduation, making research an integral part of students' education. The program is coordinated by the UNR Computer Vision Laboratory. Some program highlights are:
Inject research modules into core courses - This forms the skeleton of our model, around which we build other, more traditional approaches. Two sophomore-level and two junior-level core courses will be targeted in this project: CS202 Computer Science II, CS308 Data Structures, CS236 Introduction to Computer Engineering, and CS336 Microprocessor Engineering.
Mathematical methods for computer vision - Computer Vision is a broad-based field of computer science that requires students to understand and integrate knowledge from numerous disciplines. Computer science and electrical engineering majors, however, do not necessarily have an interdisciplinary background. In the rush to expose our students to this area, we usually forget to plan for the fact that our students may not possess an adequately broad education. In an effort to makeup for students' weak background, many instructors either spend too much time on teaching background concepts or just skip the mathematical details and proceed immediately to demos and implementation. To remedy this problem, we plan to develop a junior-level course on mathematical methods for computer vision.
Object recognition- Object recognition is probably the most important area in computer vision with many practical ramifications in modern manufacturing, autonomous navigation, and medical imaging. Although a significant body of research has been produced in this area during the last 20 years, most of the existing computer vision textbooks contain only 1-2 chapters on object recognition which are quite outdated. We plan to develop a new course in model-based object recognition at the junior- and introductory graduate-level. The content of the course will be based on recent advances in the field including our own research contributions.
Summer research - To keep the research activities of students "alive" during the summer, the program provides a chance for undergraduate and graduate students to participate in summer research (three months) in the area of Computer Vision. Participating students will work collaboratively with faculty, researchers, and other students at various sites including academic, goverment, and industry research labs. Financial assistance will be provided to well-qualified students.
Advisory board - An advisory committee has been formed by the PIs to help us in designing and evaluating the content of the modules and new courses, suggest innovative student research projects that can be integrated into the curriculum, disseminate the results of the project, and allow well-qualified students from UNR to join their research teams during the summer. The advisory committee includes academic, national lab, and industry collaborators.
Student poster program - All students who will participate in summer research will present their research at an annual poster program. The poster sessions will be attended by our academic and industrial collaborators as well as other UNR faculty and students and state representatives. When appropriate, students will present their work in regional and/or national conference. The program will cover all travel expenses and conference registration fees for this purpose.
Program Evaluation - To assess the impact of the project on students and on faculty, we will use multiple measures, both at the pre-assessment point and at the exit level. There will also be assessments done throughout the project as students move from one experience to another.
Program Dissemination - Our model, philosophy, modules, and course content, as well as feedback received from our advisory committee will be disseminated to the scientific and engineering community at large through this webpage, presentations at suitable conferences, workshops, short courses, seminars, and educational journals.
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Program related questions/comments: email@example.com