Min Sun will present his research seminar/general exam on Wednesday April 22 at 3:30PM in Room 402.

The members of his committee are;  Fei-Fei Li (advisor), David Blei, Adam Finkelstein.  Everyone is

invited to attend his talk and those faculty wishing to remain for the oral exam following are welcome

to do so.  His abstract and reading list follow below.

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We propose a novel probabilistic framework for learning visual models of 3D object categories by combining appearance information and geometric constraints. Objects are represented as a coherent ensemble of parts that are consistent under 3D viewpoint transformations. Each part is a collection of salient image features. A generative framework is used for learning a model that captures the relative position of parts within each of the discretized viewpoints. Contrary to most of the existing mixture of viewpoints models, our model establishes explicit correspondences of parts across different viewpoints of the object class. Given a new image, detection and classification are achieved by determining the position and viewpoint of the model that maximize recognition scores of the candidate objects. Our approach is among the first to propose a generative probabilistic framework for 3D object categorization. We test our algorithm on the detection task and the viewpoint classification task by using “car” category from both the Savarese et al. 2007 and PASCAL VOC 2006 datasets. We show promising results in both the detection and viewpoint classification tasks on these two challenging datasets.

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Reading list:

• Textbooks
[1] C. M. Bishop. Pattern Recognition and Machine
Learning (Information Science and Statistics).
Springer, August 2006. Chapter 1, 2, 8, and 10.
[2] D. A. Forsyth and J. Ponce. Computer Vision: A Modern
Approach. Prentice Hall, August 2002.
• Papers
[1] D. M. Blei. Variational methods for the dirichlet process.
In In Proceedings of the 21st International Conference
on Machine Learning, 2004.
[2] P. F. Felzenszwalb and D. P. Huttenlocher. Pictorial
structures for object recognition. IJCV, 61:2005, 2005.
[3] D. Hoeim, C. Rother, and J. Winn. 3d layoutcrf for
multi-view object class recognition and segmentation.
2007. In Proc. In IEEE Conference on Computer Vision
and Pattern Recognition.
[4] A. Kushal, C. Schmid, , and J. Ponce. Flexible object
models for category-level 3d object recognition.
2007. Flexible object models for category-level 3d object
recognition. In Proc. In IEEE Conf. on Comp. Vis.
and Patt. Recogn.
[5] B. Leibe, A. Leonardis, and B. Schiele. Combined object
categorization and segmentation with an implicit
shape model. In In ECCV workshop on statistical
learning in computer vision, pages 17–32, 2004.
[6] J. Liebelt, C. Schmid, and K. Schertler. Viewpointindependent
object class detection using 3d feature
maps. Computer Vision and Pattern Recognition, 2008.
CVPR 2008. IEEE Conference on, pages 1–8, June
2008.
[7] S. Savarese and L. Fei-Fei. 3d generic object categorization,
localization and pose estimation. 2007. IEEE
Intern. Conf. in Computer Vision (ICCV).
[8] E. B. Sudderth, A. Torralba, W. T. Freeman, and A. S.
Willsky. Describing visual scenes using transformed
dirichlet processes. In In NIPS, pages 1297–1304. MIT
Press, 2005.
[9] A. Thomas, V. Ferrari, B. Leibe, T. Tuytelaars,
B. Schiele, and L. V. Goo. Towards multi-view object
class detection. In Proc. In IEEE Conference on Computer
Vision and Pattern Recognition, volume 2, pages
1589-1596.
[10] P. Yan, D. Khan, and M. Shah. 3d model based object
class detection in an arbitrary view. ICCV, 2007.