Abstract
This work aims at defining an extension of a competitive method for matching correspondences in stereoscopic image analysis. The method we extended was proposed by Venkatesh, Y.V. et al where the authors extend a Self-Organizing Map by changing the neural weights updating phase in order to solve the correspondence problem within a two-frame area matching approach and producing dense disparity maps. In the present paper we have extended the method mentioned by adding some details that lead to better results. Experimental studies were conducted to evaluate and compare the solution proposed.
Keywords
References
Ballard, D.H., Brown, C.M.: Computer Vision. Prentice-Hall, Englewood Cliffs (1982)
Faugeras, O.: Three-dimensional computer vision: a geometric viewpoint. MIT Press, Cambridge (1993)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47, 7–42 (2002)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Binaghi, E., Gallo, I., Marino, G., Raspanti, M.: Neural adaptive stereo matching. Pattern Recognition Letters 25, 1743–1758 (2004)
Gallo, I., Binaghi, E., Raspanti, M.: Neural disparity computation for dense two-frame stereo correspondence. Pattern Recogn. Lett. 29(5), 673–687 (2008)
Venkatesh, Y.V., Raja, S.K., Kumar, A.J.: On the application of a modified self-organizing neural network to estimate stereo disparity. IEEE Transactions on Image Processing 16(11), 2822–2829 (2007)
Kohonen, T.: Self-organizing formation of topologically correct feature maps. Biological Cybernetics 43(1), 59–69 (1982)
Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: Proceedings. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I–195–I–202 (2003)
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
Vanetti, M., Gallo, I., Binaghi, E. (2009). Dense Two-Frame Stereo Correspondence by Self-organizing Neural Network. In: Foggia, P., Sansone, C., Vento, M. (eds) Image Analysis and Processing – ICIAP 2009. ICIAP 2009. Lecture Notes in Computer Science, vol 5716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04146-4_110
Download citation
DOI: https://doi.org/10.1007/978-3-642-04146-4_110
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04145-7
Online ISBN: 978-3-642-04146-4
eBook Packages: Computer ScienceComputer Science (R0)