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References

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.

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References

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  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 CVPR’05. [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 ECCV’02. [Burl98] M. Burl, M. Weber, P. Perona. “A probabilistic approach to object recognition using local photometry and global geometry”. In ECCV’98. [Chum05] O. Chum, O, J. Matas. “Matching with PROSAC - Progressive Sample Consensus”. CVPR’05. [Crandall05] D. Crandall, P. Felzenszwalb, D. Huttenlocher. “Spatial priors for part-based recognition using statistical models. In CVPR’05. [Csurka04] G. Csurka, C. Bray, C. Dance, L. Fan. “Visual categorization with bags of keypoints”. In ECCV’04 Workshop on Statistical Learning in Computer Vision, Prague, 2004. [Dalal05] N. Dalal B. Triggs. “Histograms of oriented gradients for human detection”. In CVPR’05. [Dorko03] G. Dorko, C. Schmid, “Selection of Scale Invariant Parts for Object Class Recognition”. In ICCV’03. [Ess07] A. Ess, B. Leibe, L. Van Gool, “Depth and Appearance for Mobile Scene Analysis”, In ICCV’07. [Ess08] A. Ess, B. Leibe, K. Schindler, L. Van Gool, “A Mobile Vision System for Robust Multi-Person Tracking”, in CVPR’08. [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 ICCV’03. 1 K. Grauman, B. Leibe K. Grauman, B. Leibe

  2. References (2) [Felzenszwalb05] P. Felzenszwalb, D. Hutenlocher. “Pictorial structures for object recognition”. International Journal of Computer Vision, 61:55-79, 2005. [Felzenszwalb08] P. Felzenszwalb, D. McAllester, D. Ramanan. “A Discriminatively Trained, Multi-Scale Deformable Part Model”, in CVPR’08. [Fergus03] R. Fergus, A. Zisserman, P. Perona. “Object class recognition by unsupervised scale-invariant learning”. In CVPR’03. [Fergus05] R. Fergus, P. Perona, A. Zisserman. “A sparse object category model for efficient learning and exhaustive recognition”. In CVPR’05. [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 CVPR’06. [Hoiem07] D. Hoiem, C. Rother, J. Winn, “3D LayoutCRF for Multi-View Object Class Recognition and Segmentation”, CVPR’07. [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 CVPR’05. [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 K. Grauman, B. Leibe

  3. References (3) [Leibe05] B. Leibe, E. Seemann, B. Schiele, “Pedestrian Detection in Crowded Scenes”, CVPR’05. [Leibe06a] B. Leibe, K. Mikolajczyk, B. Schiele, “Efficient Clustering and Matching for Object Class Recognition”, In BMVC’06. [Leibe06b] B. Leibe, K. Mikolajczyk, B. Schiele, “Segmentation Based Multi-Cue Integration for Object Detection”, In BMVC’06. [Leibe07a] B. Leibe, N. Cornelis, K. Cornelis, L. Van Gool, “Dynamic 3D Scene Analysis from a Moving Vehicle”. In CVPR’07. [Leibe07b] B. Leibe, K. Schindler, L. Van Gool, “Coupled Detection and Trajectory Estimation for Multi-Object Tracking”. In ICCV’07. [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, 14:253-277, 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 ICCV’99. [Lowe01] D. Lowe. “Local feature view clustering for 3D object recognition”. In CVPR’01. [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 ICCV’01. [Mikolajczyk04] K. Mikolajczyk, C. Schmid. “Scale and affine invariant interest point detectors”. International Journal of Computer Vision, 1(60):63–86, 2004. 3 K. Grauman, B. Leibe 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 CVPR’06. [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):321–329, 2005. [Nister06] D. Nister, H. Stewenius. “Scalable Recognition with a Vocabulary Tree”. In CVPR’06. [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”, BMVC’02. [Rowley et al.] H. Rowley, S. Baluja, and T. Kanade. Neural network-based face detection. In IEEE Patt. Anal. Mach. Intell., volume 20, pp. 22–38, 1998. [Schmid96] C. Schmid, R. Mohr, “Combining Greyvalue Invariants with Local Constraints for Object Recognition”. In CVPR’96. [Seemann06] E. Seemann, B. Leibe, B. Schiele, “Multi-Aspect Detection of Articulated Objects”. In CVPR’06. [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 ICCV’03. [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 ICCV’05. [Thomas06] A. Thomas, V. Ferrari, B. Leibe, T. Tuytelaars, B. Schiele, and L. Van Gool. “Towards Multi-View Object Class Detection”, CVPR’06. [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):61–85, 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 CVPR’01. 4 K. Grauman, B. Leibe 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 ECCV’00. [Winn05] J. Winn, N. Joijic. “Locus: Learning object classes with unsupervised segmentation”. In ICCV’05. [Yu03] S.X. Yu, J. Shi, “Object-specific Figure-Ground Segregation”. In CVPR’03. 5 K. Grauman, B. Leibe K. Grauman, B. Leibe

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