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. 2010 Jun 1;26(12):i261–i268. doi: 10.1093/bioinformatics/btq201

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© The Author(s) 2010. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Fig. 2. — An illustration of the feature point matching procedure. The goal is to match simultaneously the point sets of Component 1 and Component 2 with the Assembly point set. All point sets are shown as spheres, where the size of a sphere represents the averaged density value in a defined volume of the density map, which is within five grid voxels of the corresponding feature point. The dashed lines between the spheres represent all possible distances within each component and within the assembly. a_ik and b_jl are the distances between points i and k in Component 1 and points j and l in the Assembly, respectively. The value of the binary variable x_ij is set to 1 if point i in Component 1 matches with point j in the Assembly and x_ij is set to zero otherwise. Correspondingly, the product x_ijx_kl is 1 if distance a_ik in Component 1 matches with distance b_jl in the Assembly and is 0 otherwise. The aim of IQP is to find the best matching x_ij with maximized IQP score F(U₁, U₂, V₁, V₂) (see Section 2).