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Encouraging the development of undergraduate researchers in computer vision
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Source Annual Joint Conference Integrating Technology into Computer Science Education archive
Proceedings of the 11th annual SIGCSE conference on Innovation and technology in computer science education table of contents
Bologna, Italy
SESSION: Networking/graphics table of contents
Pages: 255 - 259  
Year of Publication: 2006
ISBN:1-59593-055-8
Also published in ...
Author
Clark F. Olson  University of Washington, Bothell, Bothell, WA
Sponsors
SIGCSE: ACM Special Interest Group on Computer Science Education
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

In a small computer science department without a graduate program, it is sometimes difficult to attract research students. This is particularly true for research in computer vision, since it is built upon a substantial body of knowledge, including considerable mathematics, that most undergraduates are not familiar with. My approach to encouraging students to take part in this research starts by introducing computation with images in early programming classes. Students become comfortable working with images in a structured framework, where they are not exposed to excessive underlying details. The students that become interested in working with images can take my computer vision class. This course is taught in a way that students can understand the material without having a deep background in mathematics. Students that are successful in this class are ready for (and encouraged to) work on undergraduate research projects and perform internships in computer vision research. While my strategy focuses on computer vision, similar approaches could be used for other research areas.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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G. Bebis, D. Egbert, and M. Shah. Review of computer vision education. IEEE Transactions on Education, 46(1):2--21, February 2003.
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E. Finak and M. Heath. Image-processing projects for an algorithms course. International Journal of Pattern Recognition and Artificial Intelligence, 15(5):859--868, August 2001.
 
4
5
6
 
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B. A. Maxwell. Teaching computer vision to computer scientists: Issues and a comparative textbook review. International Journal of Pattern Recognition and Artificial Intelligence, 12(8):1035--1051, August 1998.
 
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R. E. Montgomery. Image analysis: A group assignment in programming with breadth. In Proceedings of the ASEE/IEEE Frontiers in Education Conference, volume 2, pages 4d3.12--4d3.14, 1995.
 
9
C. F. Olson, H. Abi-Rached, M. Ye, and J. P. Hendrich. Wide-baseline stereo vision for Mars rovers. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1302--1307, October 2003.
 
10
S. Sarkar and D. Goldgof. Integrating image computation in undergraduate level data-structure education. International Journal of Pattern Recognition and Artificial Intelligence, 12(8):1071--1080, 1998.
 
11
 
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D. E. Stevenson. Image related applications for a core algorithms course. International Journal of Pattern Recognition and Artificial Intelligence, 15(5):845--857, August 2001.
 
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