Abstract
This paper presents an approach to match feature point of a pair of 3-dimensional affine model images. The affine transferring parameters are computed by a set of corresponding feature points, which are obtained based on 2D Hopfield neural network. The design of energy function of the neural network optimizes the matching error of the feature points. Two affine geometric constraints, epipolar and homography are used without the restriction to scene’s particularity. A pair of affine model images tests the performance of the proposed method.
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© 2005 Springer-Verlag Berlin Heidelberg
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Tian, J., Su, J. (2005). Feature Point Matching of Affine Model Images Using Hopfield Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_66
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DOI: https://doi.org/10.1007/11427445_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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