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References 2. Parts and Structure. [Agarwal02] S. Agarwal and D. Roth. Learning a sparse representation for object detection. In Proceedings of the 7th.

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Presentation on theme: "References 2. Parts and Structure. [Agarwal02] S. Agarwal and D. Roth. Learning a sparse representation for object detection. In Proceedings of the 7th."— Presentation transcript:

1 References 2. Parts and Structure

2 [Agarwal02] S. Agarwal and D. Roth. Learning a sparse representation for object detection. In Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, pages , [Agarwal_Dataset] Agarwal, S. and Awan, A. and Roth, D. UIUC Car dataset. ~cogcomp/Data/Car, [Amit98] Y. Amit and D. Geman. A computational model for visual selection. Neural Computation, 11(7): , [Amit97] Y. Amit, D. Geman, and K. Wilder. Joint induction of shape features and tree classi- ers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(11): , [Amores05] J. Amores, N. Sebe, and P. Radeva. Fast spatial pattern discovery integrating boosting with constellations of contextual discriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, volume 2, pages , [Bar-Hillel05] A. Bar-Hillel, T. Hertz, and D. Weinshall. Object class recognition by boosting a part based model. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, volume 1, pages , [Barnard03] K. Barnard, P. Duygulu, N. de Freitas, D. Forsyth, D. Blei, and M. Jordan. Matching words and pictures. JMLR, 3: , February [Berg05] A. Berg, T. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondence. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, volume 1, pages 26-33, June [Biederman87] I. Biederman. Recognition-by-components: A theory of human image understanding. Psychological Review, 94: , [Biederman95] I. Biederman. An Invitation to Cognitive Science, Vol. 2: Visual Cognition, volume 2, chapter Visual Object Recognition, pages MIT Press, 1995.

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4 [FeiFei03] L. Fei-Fei, R. Fergus, and P. Perona. A Bayesian approach to unsupervised one-shot learning of object categories. In Proceedings of the 9th International Conference on Computer Vision, Nice, France, pages , October [FeiFei04] L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In Workshop on Generative-Model Based Vision, [FeiFei05] L. Fei-Fei and P. Perona. A Bayesian hierarchical model for learning natural scene categories. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, volume 2, pages , June [Felzenszwalb00] P. Felzenszwalb and D. Huttenlocher. Pictorial structures for object recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages , [Felzenszwalb05] P. Felzenszwalb and D. Hutenlocher. Pictorial structures for object recognition. International Journal of Computer Vision, 61:55-79, January [Fergus_Datasets] R. Fergus and P. Perona. Caltech Object Category datasets. caltech.edu/html-files/archive.html, [Fergus03] R. Fergus, P. Perona, and P. Zisserman. Object class recognition by unsupervised scaleinvariant learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages , [Fergus04] R. Fergus, P. Perona, and A. Zisserman. A visual category lter for google images. In Proceedings of the 8th European Conference on Computer Vision, Prague, Czech Republic, pages Springer-Verlag, May [Fergus05 R. Fergus, P. Perona, and A. Zisserman. A sparse object category model for ecient learning and exhaustive recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, volume 1, pages , [Fergus_Technote] R. Fergus, M. Weber, and P. Perona. Ecient methods for object recognition using the constellation model. Technical report, California Institute of Technology, [Fischler73] M.A. Fischler and R.A. Elschlager. The representation and matching of pictorial structures. IEEE Transactions on Computer, c-22(1):67-92, Jan

5 [Grimson87] W. E. L. Grimson and T. Lozano-Perez. Localizing overlapping parts by searching the interpretation tree. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(4): , [Harris98] C. J. Harris and M. Stephens. A combined corner and edge detector. In Proceedings of the 4th Alvey Vision Conference, Manchester, pages , [Hart68] P.E. Hart, N.J. Nilsson, and B. Raphael. A formal basis for the determination of minimum cost paths. IEEE Transactions on SSC, 4: , [Helmer04] S. Helmer and D. Lowe. Object recognition with many local features. In Workshop on Generative Model Based Vision 2004 (GMBV), Washington, D.C., July [Hofmann99] T. Hofmann. Probabilistic latent semantic indexing. In SIGIR '99: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 15-19, 1999, Berkeley, CA, USA, pages ACM, [Holub05] A. Holub and P. Perona. A discriminative framework for modeling object classes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, volume 1, pages , [Kadir01] T. Kadir and M. Brady. Scale, saliency and image description. International Journal of Computer Vision, 45(2):83-105, [Kadir_Code] T. Kadir and M. Brady. Scale Scaliency Operator. ~timork/salscale.html, [Kumar05] M. P. Kumar, P. H. S. Torr, and A. Zisserman. Obj cut. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, pages 18-25, [Leibe04] B. Leibe, A. Leonardis, and B. Schiele. Combined object categorization and segmentation with an implicit shape model. In Workshop on Statistical Learning in Computer Vision, ECCV, [Leung98] T. Leung and J. Malik. Contour continuity and region based image segmentation. In Proceedings of the 5th European Conference on Computer Vision, Freiburg, Germany, LNCS 1406, pages Springer-Verlag, [Leung95] T.K. Leung, M.C. Burl, and P. Perona. Finding faces in cluttered scenes using random labeled graph matching. Proceedings of the 5th International Conference on Computer Vision, Boston, pages , June 1995.

6 [Leung98] T.K. Leung, M.C. Burl, and P. Perona. Probabilistic ane invariants for recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages , [Lindeberg98] T. Lindeberg. Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2):77-116, [Lowe99] D. Lowe. Object recognition from local scale-invariant features. In Proceedings of the 7th International Conference on Computer Vision, Kerkyra, Greece, pages , September [Lowe01] D. Lowe. Local feature view clustering for 3D object recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, pages Springer, December [Lowe04] D. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, [Mardia89] K.V. Mardia and I.L. Dryden. \Shape Distributions for Landmark Data". Advances in Applied Probability, 21: , [Sivic05] J. Sivic, B. Russell, A. Efros, A. Zisserman, and W. Freeman. Discovering object categories in image collections. Technical Report A. I. Memo , Massachusetts Institute of Technology, [Sivic03] J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In Proceedings of the International Conference on Computer Vision, pages , October [Sudderth05] E. Sudderth, A. Torralba, W. Freeman, and A. Willsky. Learning hierarchical models of scenes, objects, and parts. In Proceedings of the IEEE International Conference on Computer Vision, Beijing, page To appear, [Torralba04] A. Torralba, K. P. Murphy, and W. T. Freeman. Sharing features: ecient boosting procedures for multiclass object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, pages , 2004.

7 [Viola01] P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 511{518, [Weber00] M.Weber. Unsupervised Learning of Models for Object Recognition. PhD thesis, California Institute of Technology, Pasadena, CA, [Weber00a] M. Weber, W. Einhauser, M. Welling, and P. Perona. Viewpoint-invariant learning and detection of human heads. In Proc. 4th IEEE Int. Conf. Autom. Face and Gesture Recog., FG2000, pages 20{27, March [Weber00b] M. Weber, M. Welling, and P. Perona. Towards automatic discovery of object categories. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2101{2108, June [Weber00c] M. Weber, M. Welling, and P. Perona. Unsupervised learning of models for recognition. In Proc. 6th Europ. Conf. Comp. Vis., ECCV2000, volume 1, pages 18{32, June [Welling05] M. Welling. An expectation maximization algorithm for inferring oset-normal shape distributions. In Tenth International Workshop on Articial Intelligence and Statistics, [Winn05] J. Winn and N. Joijic. Locus: Learning object classes with unsupervised segmentation. In Proceedings of the IEEE International Conference on Computer Vision, Beijing, page To appear, 2005


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