Abstract
This paper proposes a hierarchical model for the recognition of deformable objects. Object categories are modelled by multiple views, views in turn consist of several parts, and parts consist of several features. The main advantage of the proposed model is that its nodes can be tuned with regard to the spatial selectivity. Every node in a category, views or part can thus take on the shape of a simple bag of features or a geometrically selective constellation model including all forms in between. Together with the explicit modelling of multiple views this allows for the modelling of categories with high intra-class variance. Experimental results show a high precision for the recognition of a character from a cartoon data base.
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References
Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 111–122 (1981)
Burge, M., Burger, W.: Learning Visual Ideals. In: Proc. of the 9th ICIAP, Florence, Italy, pp. 316–323 (1997)
Crandall, D.J., Felzenszwalb, P.F., Huttenlocher, D.P.: Spatial Priors for Part-Based Recognition Using Statistical Models. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10–17 (2005)
Disney, W.: Lustiges Taschenbuch. vol. 204, 320, 323, 327, 328, 336, 357, 367, Spezial 13, Enten Edition 7, 20, Sonderband 12, Egmont Ehapa, Berlin, Germany
Fergus, R., Perona, P., Zisserman, A.: A Sparse Object Category Model for Efficient Learning and Complete Recognition. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. LNCS, vol. 4170, pp. 443–461. Springer, Heidelberg (2006)
Fergus, R., Perona, P., Zisserman, A.: Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition. International Journal of Computer Vision 71(3), 273–303 (2007)
Hubel, D.H.: Eye, Brain and Vision. Scientific American Library (1988)
Leibe, B., Schiele, B.: Analyzing Contour and Appearance Based Methods for Object Categorization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2003)
Marr, D.: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman and Company, New York (1982)
Mikolajczyk, K., Leibe, B., Schiele, B.: Multiple Object Class Detection with a Generative Model. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2006) (June 2006)
Murase, H., Nayar, S.K.: Visual learning and recognition of 3-d objects from appearance. Int. Journal of Computer Vision 14(1), 5–24 (1995)
Ommer, B., Buhmann, J.M.: Object Categorization by Compositional Graphical Models. In: Rangarajan, A., Vemuri, B.C., Yuille, A.L. (eds.) EMMCVPR 2005. LNCS, vol. 3757, pp. 103–113. Springer, Heidelberg (2005)
Rolls, E.T., Treves, A., Tovee, M.J.: The representational capacity of the distributed encoding of information provided by populations of neurons in primate temporal visual cortex. Experimental Brain Research 114(1), 149–162 (1997)
Schein, S.J., Desimone, R.: Spectral Properties of V4 Neurons in the Macaque. Journal of Neuroscience 10(10), 3369–3389 (1990)
Serre, T., Wolf, L., Poggio, T.: Object recognition with features inspired by visual cortex. In: Proc. of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 994–1000. IEEE Computer Society, San Diego (2005)
Stommel, M., Kuhnert, K.-D.: Appearance based recognition of complex objects by genetic prototype-learning. In: Proc. 13th Int’l Conf. In: Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), Plzen, Czech Republic, January 31–February 4 (2005)
Stommel, M., Kuhnert, K.-D.: A Learning Algorithm for the Appearance-Based Recognition of Complex Objects. World Congress in Computer Science, Computer Engineering, and Applied Computing (WORLDCOMP 2006). In: Proc. The 2006 International Conference on Machine Learning; Models, Technologies & Application (MLMTA 2006), Las Vegas, Nevada, USA (2006)
Tanaka, K.: Inferotemporal cortex and object vision. Annual Reviews of Neuroscience 19, 109–139 (1996)
Trappenberg, T.P., Rolls, E.T., Stringer, S.M.: Effective Size of Receptive Fields of Inferior Temporal Visual Cortex Neurons in Natural Scenes. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14(1), pp. 293–300. MIT Press, Cambridge (2001)
Turk, M., Pentland, A.: Face recognition using eigenfaces. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)
Weber, M., Welling, M., Perona, P.: Unsupervised Learning of Models for Recognition. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 18–32. Springer, Heidelberg (2000)
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Stommel, M., Kuhnert, KD. (2009). A Hierarchical Model for the Recognition of Deformable Objects. In: Bolc, L., Kulikowski, J.L., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2008. Lecture Notes in Computer Science, vol 5337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02345-3_40
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DOI: https://doi.org/10.1007/978-3-642-02345-3_40
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