Abstract
Engineering approaches to stereo typically use explicit search for the best matching between left and right sub-windows, which involves a high cost of search and unstable performance in the presence of binocular inconsistency and weak texture. The brain does not seem to conduct explicit search in the V1 and V2 cortex. But the mechanisms that the brain employs to integrate binocular disparity into 3-D perception is still largely a mystery. The work presented in this paper focuses on an important issue of integrated stereo: How the same cortex can perform recognition and perception by generating a topographic disparity-tuning map using top-down connections. As top-down connections with object-class supervisory signals result in topographic class maps, the model presented here clarifies that stereo can be processed by a unified in-place learning framework in the neural layers, and can generate iconic-abstract internal representation.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Parker, A.J.: Binocular depth perception and the cerebral cortex. Nature Reviews Neuroscience 8(5), 379–391 (2007)
Dhond, U.R., Aggarwal, J.K.: Structure from stereo - a review. IEEE Transactions on Systems, Man and Cybernetics 19(6), 1489–1510 (1989)
Zitnick, C.L., Kanade, T.: A cooperative algorithm for stereo matching and occlusion detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(7), 675–684 (2000)
Fleet, D.J., Jepson, A.D., Jenkin, M.R.M.: Phase-based disparity measurement. In: CVGIP: Image Understand, vol. 53, pp. 198–210 (1991)
Weng, J.: Image matching using the windowed Fourier phase. International Journal of Computer Vision 11(3), 211–236 (1993)
Lehky, S.R., Sejnowski, T.J.: Neural model of stereoacuity and depth interpolation based on a distributed representation of stereo disparity. The Journal of Neuroscience 70(7), 2281–2299 (1990)
Lippert, J., Fleet, D.J., Wagner, H.: Disparity tuning as simulated by a neural net. Journal of Biocybernetics and Biomedical Engineering 83, 61–72 (2000)
Wiemer, J., Burwick, T., Seelen, W.: Self-organizing maps for visual feature representation based on natural binocular stimuli. Biological Cybernetics 82(2), 97–110 (2000)
Franz, A., Triesch, J.: Emergence of disparity tuning during the development of vergence eye movements. In: International Conference on Development and Learning, pp. 31–36 (2007)
Marr, D.: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. Freeman, New York (1982)
Bi, G., Poo, M.: Synaptic modification by correlated activity: Hebb’s postulate revisited. Annual Review of Neuroscience 24, 139–166 (2001)
Roelfsema, P.R., van Ooyen, A.: Attention-gated reinforcement learning of internal representations for classification. Journal of Neural Computation 17, 2176–2214 (2005)
Sit, Y.F., Miikkulainen, R.: Self-organization of hierarchical visual maps with feedback connections. Neurocomputing 69, 1309–1312 (2006)
Weng, J., Luciw, M.D.: Optimal in-place self-organization for cortical development: Limited cells, sparse coding and cortical topography. In: Proc. 5th International Conference on Development and Learning (ICDL 2006), Bloomington, IN, May 31-June 3 (2006)
Weng, J., Luwang, T., Lu, H., Xue, X.: Multilayer in-place learning networks for modeling functional layers in the laminar cortex. Neural Networks 21, 150–159 (2008)
Luciw, M., Weng, J.: Topographic class grouping with applications to 3D object recognition. In: Proc. International Joint Conf. on Neural Networks, Hong Kong (June 2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Solgi, M., Weng, J. (2009). Developmental Stereo: Topographic Iconic-Abstract Map from Top-Down Connection. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-02490-0_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02489-4
Online ISBN: 978-3-642-02490-0
eBook Packages: Computer ScienceComputer Science (R0)