Abstract
What are the primitives of visual perception? The early feature-analysis theory insists on it being a local-to-global process which has acted as the foundation of most computer vision applications for the past 30 years. The early holistic registration theory, however, considers it as a global-to-local process, of which Chen’s theory of topological perceptual organization (TPO) has been strongly supported by psychological and physiological proofs. In this paper, inspired by Chen’s theory, we propose a novel visual organization, termed computational topological perceptual organization (CTPO), which pioneers the early holistic registration in computational vision. Empirical studies on synthetic datasets prove that CTPO is invariant to global transformation such as translation, scaling, rotation and insensitive to topological deformation. We also extend it to other applications by integrating it with local features. Experiments show that our algorithm achieves competitive performance compared with some popular algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Marr, D.: Representing visual information: A computational approach. Lectures on Mathematics in the Life Science 10, 61–80 (1978)
Marr, D.: A computational investigation into the human representation and processing of visual information. Freeman, San Francisco (1982)
Lowe, D.G.: Distinctive image features from dcale invariant key-points. International Journal of Computer Vision 2(60), 91–110 (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)
Filed, D.J., Hayes, A., Hess, R.F.: Good continuation and the association field: Evidence for localfeature integration by the visual system. Vision Research 33, 173–193 (1993)
Chen, L.: Topological structure in visual perception. Science 218, 699–700 (1982)
Chen, L.: The topological approach to perceptual organization. Visual Cognition 12(4), 553–638 (2005)
Klein, F.: A comparative review of recent researches in geometry. Mathematische Annalen 43, 63–100 (1872)
Zhuo, Y., Zhou, T.G., Rao, H.Y., Wang, J.J., Meng, M., Chen, M., Zhou, C., Chen, L.: Contributions of the visual ventral pathway to long-range apparent motion. Science 299, 417–420 (2003)
Todd, J., et al.: Commentaries: stability and change. Visual Cognition 12(4), 639–690 (2005)
Chen, L.: Reply: Author’s response: Where to begin? Visual Cognition 12(4), 691–701 (2005)
Ling, H.B., Jacobs, D.W.: Deformation invariant image matching. In: ICCV (2005)
Ling, H.B., Jacobs, D.W.: Shape classification using the inner-distance. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(2), 286–299 (2007)
Huang, Y.Z., Huang, K.Q., Tao, D.C., Wang, L.S., Tan, T.N., Li, X.L.: Enhanced biological inspired model. In: CVPR (2008)
Tenenbaum, J.B., Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality. Science 290(22), 2319–2323 (2000)
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust object recognition with cortex-like mechanisms. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(3) (2007)
Bileschi, S., Wolf, L.: Image representations beyond histograms of gradients: The role of gestalt descriptors. In: CVPR (2007)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Opelt, A., Pinz, A., Fussenegger, M., Auer, P.: Generic object recognition with boosting. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(3), 416–431 (2006)
Leordeanu, M., Hebert, M., Sukthankar, R.: Beyond local appearance: Category recognition from pairwise interactions of simple features. In: CVPR (2007)
Ling, H., Soatto, S.: Proximity distribution kernels for geometric context in category recognition. In: ICCV (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huang, Y., Huang, K., Tan, T., Tao, D. (2010). A Novel Visual Organization Based on Topological Perception. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12307-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-12307-8_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12306-1
Online ISBN: 978-3-642-12307-8
eBook Packages: Computer ScienceComputer Science (R0)