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Requirements
- 1 page, single space (1inch borders), 11pt paper critic from every student
- 1 hour presentation by the group of 2 people
- Questions, answers and discussion
- Program demo in the class
Ranking of presentations
- Every student should rank (1st rank is the best) the presentations (except for theirs) at the end of quarter.
- The rankings are kept confidential and will not be disclosed
- Every ranked presentation should have an accompanying score. The scores are 1 to 5, 1 being the best 5 being acceptable.
- The scores can be the same for different presentations but rankings must be different.
- Your rankings and scores will have 30% weight and instructors rankings will have 70% weight during the evaluations.
Papers:
- Sudhagar and Sajid on KDE:
D. Comaniciu and P. Meer. Mean shift analysis and applications. In IEEE Int. Conf. on Computer Vision, volume 2, pages 1197-1203, 1999
- Shaojun and Ekta on MLE:
C. Olson. Maximum-likelihood template matching. In IEEE Conf. on Computer Vision and Pattern Recognition, volumeÊ2, pages 52-57, 2000
- I-Chiehand Lin Yan on HMM:
J. Kato, T. Watanabe, S. Joga, and A. Blake. An hmm-based segmentation method for traffic monitoring movies. pami, 24(9):1291-1296, 2002
- Nikki and Li-ju on PCA:
M. Turk and A. Pentland (1991). "Eigenfaces for recognition". Journal of Cognitive Neuroscience 3 (1): 71Ð86
- Yeon and Falko on BE:
H. Tao, H. Sawhney, and R. Kumar. Object tracking with bayesian estimation of dynamic layer representations. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(1):75-89, 2002
- Young-jin and Yuksel on Bayesian methods:
Wang, Lodha and Helmbold, A Bayesian Approach to Building Footprint Extraction from Aerial LIDAR Data
- Yunhang and Jinwei on AdaBoost:
Viola, Jones and Snow. Detecting Pedestrians Using Patterns of Motion and Appearance. ICCV 2003
- Panu and Gabor on Markov Models:
N. Thakoor and J. Gao. Shape Classifier based on generalized probabilistic descent method with hidden markov descriptor
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