CS 684 Object Recogntion-Fall 2005
Note: Course has been moved to the
Viz-Center and now meets MWF 0900-0950 in the Viz-Center conference room,
8th floor, Center for
Visualization
and Virtual Environments University of Kentucky, 1 Quality Street,
Suite 800.
Syllabus
Syllabus(pdf)
General Info
This is a reading class on visual object recognition. It will focus
mainly on recognition with invariant local regions and descriptors.
Homework
There will be three assignments.
Assignment 1:
Collect 4 images (no more, no less, it is important that the image
names come up in groups of 4 upon an ls) of each of 100 objects. (i.e.
4x100=400 images).
The images should be 640x480, JPEG with low compression. Examples
Assignment 2:
Recognition (Individual)
Assignment 3:
Recognition (Group)
Links
Here are the papers that we will read in class:
[1]
D. Lowe. Distinctive image features from scale-invariant keypoints. Int.
Journal of Computer Vision,
60(2):91–110, 2004.
[2]
J. Matas, O. Chum, U. Martin, and T. Pajdla. Robust wide baseline
stereo from
maximally stable extremal regions. In Proc. British
Machine Vision Conference, volume 1, pages 384–393,
Sep 2002.
[3]
K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point
detectors. Int. Journal of Computer
Vision, 1(60):63–86, 2004
[4]
T.Tuytelaars and L. Van Gool. Matching widely separated views based on
affine
invariant regions. Int. Journal of Computer
Vision, 1(59):61–85, 2004
[5]
K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F.
Schaffalitzky, T. Kadir, and L. Van Gool. A comparison of affine region
detectors. Technical Report,
accepted
to IJCV, 2005
[6] Stephan
Obdrzalek and Jiri Matas Object Recognition using Local
Affine Frames
on
Distinguished Regions, BMVC 02, http://cmp.felk.cvut.cz/~matas/papers/obdrzalek-bmvc02.pdf
[7] Stephan
Obdrzalek and Jiri Matas,Image Retrieval Using
Local Compact DCT-based Representation, DAGM’03, 25th
Pattern Recognition Symposium
September 10-12, 2003, Magdeburg, Germany,
http://cmp.felk.cvut.cz/~matas/papers/obdrzalek-dagm03.pdf
[8]
M. Brown, R. Szeliski, and S. Winder. Multi-image matching using
multi-scale
oriented patches. In IEEE
Computer Society Conference on Computer Vision and Pattern Recognition
(CVPR'2005), volume I, pages 510-517, San
Diego, CA, June 2005.
[9]
S. Belongie, J. Malik, and J. Puzicha. Shape matching and object
recognition using shape contexts. IEEE Transactions
on Pattern Analysis and Machine Intelligence,
24(4):509.522, April 2002.
[10]
K. Mikolajczyk and C. Schmid. A performance evaluation of local
descriptors. Technical
Report, accepted to PAMI, 2005
[11]
J. Sivic and A. Zisserman. Video Google: A text retrieval approach to
object
matching in
videos.
In Proc.
International Conference on Computer Vision,
Oct 2003
[12]
Stephan Obdrzalek and Jiri Matas, Sub-linear Indexing
for Large Scale Object
Recognition Accepted for
publication at
British Machine Vision Conference (BMVC) 2005
September
2005, Oxford, UK,
http://cmp.felk.cvut.cz/~matas/papers/obdrzalek-tree-bmvc05.pdf
[13] V.
Lepetit, P. Lagger, and P. Fua. Randomized trees for real-time keypoint
recognition. In Proc. IEEE Conference on
Computer Vision and Pattern Recognition,
Jun 2005
<>[14] Wolfson and Rigoutsos,
Geometric
Hashing: An Overview,
http://graphics.stanford.edu/courses/cs468-01-winter/papers/wr-ghao-97.pdf
<>[15] Indyk,
P. and Motwani, R. (1998)
Approximate nearest neighbors: Towards removing the curse of
dimensionality. In Proc. 30th Ann. ACM Symp.
on Theory of Computing,
Dallas, TX.
[16] Alexander C.
Berg, Tamara L.
Berg, Jitendra
Malik, Shape
Matching and Object Recognition using Low Distortion Correspondence,
CVPR 2005
[17] F. Rothganger, S. Lazebnik, C.
Schmid, and J. Ponce. Object modeling and
recognition using local affine-invariant image descriptors and
multi-view
spatial contraints. Accepted for IJCV.
[18]
Jiri Matas and Ondrej Chum. Randomized ransac with T_d test.
Image and Vision Computing,
22(10):837-842, September 2004
[19]
Greg Mori, Jitendra Malik, Recognizing
Objects in Adversarial Clutter: Breaking a Visual CAPTCHA (2003).
http://www.cs.berkeley.edu/~mori/
[20]
Maximilian Riesenhuber and Tomaso Poggio, "Models of object
recognition," Nature
Neuroscience, 3 (2000), pp. 1199 - 1204.
[21]
Freund and Schapire, "A short introduction to boosting," JJSAI, 1999
[22]
Paul Viola and Michael Jones, "Rapid object detection using a boosted
cascade of simple features," Conference
on Computer Vision and Pattern Recognition, 2001, pp.
511-518.
[23] Jan Sochman
Jiri
Matas, WaldBoost
– Learning for Time Constrained Sequential
Detection, CVPR 05,
http://cmp.felk.cvut.cz/~matas/papers/sochman-waldboost-cvpr05.pdf
[24] Rob Fergus, Pietro
Perona, and Andrew Zisserman,
"Object Class Recognition by Unsupervised Scale-Invariant Learning," Conference on Computer Vision and Pattern
Recognition, (2003)
[25] K.
Grauman and T. Darrell. The Pyramid Match Kernel: Discriminative
Classification with Sets of Image Features. To appear, Proceedings
of
the IEEE International Conference on Computer Vision, Beijing,
China, October 2005.
[26]
A.A. Efros and T.K. Leung, "Texture Synthesis by Non-parametric
Sampling," International Conference on Computer Vision (1999).
[27] Erik B. Sudderth,
Antonio Torralba, William T. Freeman, and Alan S. Willsky, Learning
Hierarchical Models of Scenes, Objects, and Parts, ICCV 2005
[28]
Xin Fan, Chun Qi Dequn Liang Hua Huang, Probabilistic Contour
Extraction Using Hierarchical Shape Representation
[29] Epshtein,
Ullman, Identifying
Semantically Equivalent Object Fragments
[30] Histograms of
Oriented Gradients for Human Detection, N. Dalal and B. Triggs. CVPR 2005
I:pp.886-893.
[31] Robert
Strzodka, et al A
Graphics Hardware Implementation of the Generalized Hough Transform for fast Object Recognition,
Scale, and 3D Pose Detection.
[32] Dirk Walther,
Ueli Rutishauser, Christof Koch, Pietro Perona, Selective visual
attention enables learning and recognition of multiple objects in
cluttered scenes