References

1
References

• Agarwal04 S. Agarwal, D. Roth. Learning to
  detect objects in images via a sparse, part-based
  representation. IEEE Trans. Pattern Analysis and
  Machine Intelligence, 26(11)1475-1490, 2004.
• Ballard81 D.H. Ballard, Generalizing the Hough
  Transform to Detect Arbitrary Shapes. Pattern
  Recognition, 13(2)111-122, 1981.
• Bar-Hillel05 A. Bar-Hillel, T. Hertz, D.
  Weinshall. Object class recognition by boosting
  a part based model. In CVPR05.
• Belongie et al. 2001 S. Belongie, J. Malik, and
  J. Puzicha. Matching Shapes, in ICCV 2001.
• Beis Lowe J. Beis D. Lowe. Shape indexing
  using approximate nearest-neighbour search in
  high-dimensional spaces, in CVPR 1997.
• Berg et al. 2005 A. Berg, T. Berg, and J.
  Malik. Shape Matching and Object Recognition
  using Low Distortion Correspondence. In CVPR
  2005.
• Borenstein02 E. Borenstein, S. Ullman.
  Class-specic, top-down segmentation. In
  ECCV02.
• Burl98 M. Burl, M. Weber, P. Perona. A
  probabilistic approach to object recognition
  using local photometry and global geometry. In
  ECCV98.
• Chum05 O. Chum, O, J. Matas. Matching with
  PROSAC - Progressive Sample Consensus. CVPR05.
• Crandall05 D. Crandall, P. Felzenszwalb, D.
  Huttenlocher. Spatial priors for part-based
  recognition using statistical models. In CVPR05.
  
• Csurka04 G. Csurka, C. Bray, C. Dance, L. Fan.
  Visual categorization with bags of keypoints.
• In ECCV04 Workshop on Statistical Learning in
  Computer Vision, Prague, 2004.
• Dalal05 N. Dalal B. Triggs. Histograms of
  oriented gradients for human detection. In
  CVPR05.
• Dorko03 G. Dorko, C. Schmid, Selection of
  Scale Invariant Parts for Object Class
  Recognition. In ICCV03.
• Ess07 A. Ess, B. Leibe, L. Van Gool, Depth
  and Appearance for Mobile Scene Analysis, In
  ICCV07.
• Ess08 A. Ess, B. Leibe, K. Schindler, L. Van
  Gool, A Mobile Vision System for Robust
  Multi-Person Tracking, in CVPR08.
• Everingham06 M. Everingham et al. (34 authors),
  The 2005 PASCAL Visual Object Class Challenge.
  In Selected Proceedings of the 1st PASCAL
  Challenges Workshop, LNAI, Springer, 2006.
• Everingham et al. 2006 M. Everingham, J. Sivic,
  and A. Zisserman. 'Hello! My name is... Buffy' -
  Automatic naming of characters in TV video. In
  BMVC 2006.
• FeiFei03 L. Fei-Fei, R. Fergus, P. Perona. A
  Bayesian approach to unsupervised one-shot
  learning of object categories. In ICCV03.

1
K. Grauman, B. Leibe
2
References (2)

• Felzenszwalb05 P. Felzenszwalb, D. Hutenlocher.
  Pictorial structures for object recognition.
  International Journal of Computer Vision,
  6155-79, 2005.
• Felzenszwalb08 P. Felzenszwalb, D. McAllester,
  D. Ramanan. A Discriminatively Trained,
  Multi-Scale Deformable Part Model, in CVPR08.
• Fergus03 R. Fergus, A. Zisserman, P. Perona.
  Object class recognition by unsupervised
  scale-invariant learning. In CVPR03.
• Fergus05 R. Fergus, P. Perona, A. Zisserman. A
  sparse object category model for efficient
  learning and exhaustive recognition. In CVPR05.
• Ferrari06 V. Ferrari, T. Tuytelaars, L. Van
  Gool. Simultaneous Object Recognition and
  Segmentation from Single or Multiple Model
  Views. International Journal of Computer Vision,
  67(2)159-188, 2006.
• Fischler73 M.A. Fischler, R.A. Elschlager. The
  representation and matching of pictorial
  structures. IEEE Transactions on Computer,
  22(1)67-92, Jan. 1973.
• Fleuret Geman F. Fleuret and D. Geman.
  Coarse-to-fine face detection In IJCV 2001.
• Gavrila Philomin D. Gavrila and V. Philomin,
  Real-time object detection for smart vehicle,
  ICCV 1999.
• Grauman Darrell 2005 K. Grauman and T.
  Darrell. The Pyramid Match Kernel Discriminative
  Classification with Sets of Image Features. In
  ICCV 2005.
• Grauman Darrell 2006 K. Grauman and T.
  Darrell. Unsupervised Learning of Categories
  from Sets of Partially Matching Image Features.
  In CVPR 2006.
• Harris98 C. J. Harris, M. Stephens. A combined
  corner and edge detector. In Proc. 4th Alvey
  Vision Conference, Manchester, 1988.
• Hoiem05 D. Hoiem, A. Efros, M. Hebert. Putting
  Objects into Perspective, in CVPR06.
• Hoiem07 D. Hoiem, C. Rother, J. Winn, 3D
  LayoutCRF for Multi-View Object Class Recognition
  and Segmentation, CVPR07.
• Hough62 P.V.C. Hough, Method and Means for
  Recognizing Complex Patterns. U.S. Patent
  3069654, 1962.
• Indyk Motwani P. Indyk and R. Motwani.
  Approximate Nearest Neighbors Towards Removing
  the Curse of Dimensionality, in STOC, 1998.
• Kadir01 T. Kadir, M. Brady. Scale, saliency
  and image description. International Journal of
  Computer Vision, 45(2)83-105, 2001.
• Kumar05 M. P. Kumar, P. H. S. Torr, A.
  Zisserman. Obj cut. In CVPR05.
• Lazebnik et al. 2003 S. Lazebnik, C. SChmid,
  and J. Ponce. Affine-Invariant Local
  Descriptors and Neighborhood Statistics for
  Texture Recognition, In ICCV 2003.
• Lazebnik et al. 2006 S. Lazebnik, C. Schmid,
  and J. Ponce. Beyond Bags of Features Spatial
  Pyramid Matching for Recognizing Natural Scene
  Categories. In CVPR 2006.

2
K. Grauman, B. Leibe
3
References (3)

• Leibe05 B. Leibe, E. Seemann, B. Schiele,
  Pedestrian Detection in Crowded Scenes,
  CVPR05.
• Leibe06a B. Leibe, K. Mikolajczyk, B.
  Schiele, Efficient Clustering and Matching for
  Object Class Recognition, In BMVC06.
• Leibe06b B. Leibe, K. Mikolajczyk, B.
  Schiele, Segmentation Based Multi-Cue
  Integration for Object Detection, In BMVC06.
• Leibe07a B. Leibe, N. Cornelis, K. Cornelis, L.
  Van Gool, Dynamic 3D Scene Analysis from a
  Moving Vehicle. In CVPR07.
• Leibe07b B. Leibe, K. Schindler, L. Van Gool,
  Coupled Detection and Trajectory Estimation for
  Multi-Object Tracking. In ICCV07.
• Leibe08 B. Leibe, A. Leonardis, B. Schiele,
  Robust Object Detection with Interleaved
  Categorization and Segmentation, in
  International Journal of Computer Vision,
  ??(1-3), 2008.
• Leordeanu Hebert M. Leordeanu and M. Hebert.
  A Spectral Technique for Correspondence Problems
  using Pairwise Constraints. In ICCV 2005.
• Leonardis95 A. Leonardis, A. Gupta, R. Bajcsy,
  Segmentation of Range Images as the Search for
  Geometric Parametric Models. International
  Journal of Computer Vision, 14253-277, 1995.
• Leung Malik T. Leung J. Malik. Recognizing
  surfaces using three dimensional textons In
  ICCV 1999.
• Lindeberg98 T. Lindeberg. Feature detection
  with automatic scale selection. International
  Journal ofComputer Vision, 30(2)77-116, 1998.
• Lowe99 D. Lowe. Object recognition from local
  scale-invariant features. In ICCV99.
• Lowe01 D. Lowe. Local feature view clustering
  for 3D object recognition. In CVPR01.
• Lowe04 D. Lowe. Distinctive image features
  from scale-invariant keypoints. International
  Journal of Computer Vision, 60(2)91-110, 2004.
• Matas02 J. Matas, O. Chum, M. Urban, T. Pajdla.
  Robust Wide Baseline Stereo from Maximally
  Stable Extremal Regions. BMVC 2002.
• Mikolajczyk01 K. Mikolajczyk, C. Schmid.
  Indexing based on Scale Invariant Interest
  Points. In ICCV01.
• Mikolajczyk04 K. Mikolajczyk, C. Schmid. Scale
  and affine invariant interest point detectors.
  International Journal of Computer Vision,
  1(60)6386, 2004.

3
K. Grauman, B. Leibe
4
References (4)

• Mikolajczyk05 K. Mikolajczyk, C. Schmid. A
  Performance Evaluation of Local Descriptors.
  IEEE Trans. PAMI, Vol. 27(10), 2005.
• Mikolajczyk06 K. Mikolajczyk, B. Leibe, B.
  Schiele, Multiple Object Class Detection with a
  Generative Model. In CVPR06.
• Mutch Lowe J. Mutch and D. Lowe. Multiclass
  object recognition with sparse, localized
  features, CVPR 2006.
• Nister05 D. Nistér, Preemptive RANSAC for Live
  Structure and Motion Estimation, Machine Vision
  and Applications, 16(5)321329, 2005.
• Nister06 D. Nister, H. Stewenius. Scalable
  Recognition with a Vocabulary Tree. In CVPR06.
• Nowak et al. E. Nowak, F. Jurie, and B. Triggs.
  Sampling Strategies for Bag-of-Features Image
  Classification. In ECCV 2006.
• Obdrzalek02 S. Obdrzalek, J. Matas. Object
  Recognition using Local Affine Frames on
  Distinguished Regions, BMVC02.
• Rowley et al. H. Rowley, S. Baluja, and T.
  Kanade. Neural network-based face detection. In
  IEEE Patt. Anal. Mach. Intell., volume 20, pp.
  2238, 1998.
• Schmid96 C. Schmid, R. Mohr, Combining
  Greyvalue Invariants with Local Constraints for
  Object Recognition. In CVPR96.
• Seemann06 E. Seemann, B. Leibe, B. Schiele,
  Multi-Aspect Detection of Articulated Objects.
  In CVPR06.
• Serre et al. 2005 T. Serre, L. Wolf, and T.
  Poggio. Object recognition with features
  inspired by visual cortex, CVPR 2005.
• Sivic03 J. Sivic, A. Zisserman. Video Google
  A text retrieval approach to object matching in
  videos. In ICCV03.
• Sivic et al. 2005 J. Sivic, B. Russell, A.
  Efros, A. Zisserman, and W. Freeman. Discovering
  Objects and Their Location in Images. In ICCV
  2005.
• Sudderth05 E. Sudderth, A. Torralba, W.
  Freeman, A. Willsky. Learning hierarchical
  models of scenes, objects, and parts. In
  ICCV05.
• Thomas06 A. Thomas, V. Ferrari, B. Leibe, T.
  Tuytelaars, B. Schiele, and L. Van Gool. Towards
  Multi-View Object Class Detection, CVPR06.
• Turk Pentland M. Turk and A. Pentland. Face
  Recognition Using Eigenfaces, CVPR 1991.
• Tuytelaars04 T.Tuytelaars, L. Van Gool.
  Matching widely separated views based on affine
  invariant regions. International Journal of
  Computer Vision, 1(59)6185, 2004.
• Varma Zisserman M. Varma and A. Zisserman.
  Classifying images of materials Achieving
  viewpoint and illumination independence. In
  ECCV 2002.
• Viola01 P. Viola, M. Jones. Rapid object
  detection using a boosted cascade of simple
  features. In CVPR01.

4
K. Grauman, B. Leibe
5
References (5)

• Wallraven et al. 2003 C. Wallraven, B. Caputo,
  A. Graf. Recognition with Local features the
  kernel recipe. ICCV03.
• Weber00 M. Weber, M. Welling, P. Perona.
  Unsupervised learning of models for
  recognition. In ECCV00.
• Winn05 J. Winn, N. Joijic. Locus Learning
  object classes with unsupervised segmentation.
  In ICCV05.
• Yu03 S.X. Yu, J. Shi, Object-specific
  Figure-Ground Segregation. In CVPR03.

5
K. Grauman, B. Leibe