Supplemental material for: Entropic Metric Alignment for Correspondence Problems Figure 1: 2D shape matching; color on the boxed model is transferred to the remaining models (α ≡ 7.5 × 10 -4 ). Source [Aflalo et al.] Ours Figure 2: Comparison to [Aflalo et al. 2015]. Here, we compute a map between 2D airplane shapes (data from [Thakoor et al. 2007]), using Euclidean distances in D0 and D. The first row shows points marked on the source shape, and the second row shows their mapped targets using their method (top) and ours (bottom). GW α successfully recovers a near-bijective map, while [Aflalo et al. 2015] superposes some but not all symmetries (compare columns 2 and 5). References AFLALO, Y., BRONSTEIN, A., AND KIMMEL, R. 2015. On convex relaxation of graph isomorphism. Proc. National Academy of Sci. 112, 10, 2942–2947. THAKOOR, N., GAO, J., AND J UNG, S. 2007. Hidden Markov model-based weighted likelihood discriminant for 2-D shape classification. Trans. Image Proc. 16, 11, 2707–2719.
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Supplemental material for:
Entropic Metric Alignment for Correspondence Problems
Figure 1: 2D shape matching; color on the boxed model is transferred to the remaining models (α ≡ 7.5× 10−4).
Sour
ce[A
flalo
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.]O
urs
Figure 2: Comparison to [Aflalo et al. 2015]. Here, we compute a map between 2D airplane shapes (data from [Thakoor et al. 2007]), usingEuclidean distances in D0 and D. The first row shows points marked on the source shape, and the second row shows their mapped targetsusing their method (top) and ours (bottom). GWα successfully recovers a near-bijective map, while [Aflalo et al. 2015] superposes some butnot all symmetries (compare columns 2 and 5).
References
AFLALO, Y., BRONSTEIN, A., AND KIMMEL, R. 2015. On convexrelaxation of graph isomorphism. Proc. National Academy of Sci.112, 10, 2942–2947.
THAKOOR, N., GAO, J., AND JUNG, S. 2007. Hidden Markovmodel-based weighted likelihood discriminant for 2-D shapeclassification. Trans. Image Proc. 16, 11, 2707–2719.
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