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Advanced Image Matching Framework for Enhanced Convergence

Explore a cutting-edge framework combining direct and feature-based costs for precise image matching with heightened convergence rates. Witness upgraded variational optical flow with robust feature integration, minimizing errors to a mere 9%. Presented by experts from CEA, LIST, and Université d'Auvergne.

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Advanced Image Matching Framework for Enhanced Convergence

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  1. Poster Spotlights Session 1A: Tuesday Morning, December 3rd A GeneralDense Image Matching FrameworkCombiningDirect and Feature-Based Costs Jim Braux-Zin, CEA, LIST, France Romain Dupont, CEA, LIST, France Adrien Bartoli, Université d'Auvergne, France

  2. Example Variational optical flow with local minima + Feature matches = Enlarged convergence basin P1A-20 A GeneralDense Image Matching FrameworkCombiningDirect and Feature-Based Costs Idea: upgrade variational optical flow with a robust feature-based term ERROR: 15% Error: 9%

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