Complementary Optic Flow

Authors:
Henning Zimmer;Andrés Bruhn;Joachim Weickert;Levi Valgaerts;Agustín Salgado;Bodo Rosenhahn;Hans-Peter Seidel
Affiliations:
Mathematical Image Analysis Group Faculty of Mathematics and Computer Science, Saarland University, Saarbrücken, Germany and Max-Planck Institute for Informatics, Saarbrücken, Germany;Mathematical Image Analysis Group Faculty of Mathematics and Computer Science, Saarland University, Saarbrücken, Germany;Mathematical Image Analysis Group Faculty of Mathematics and Computer Science, Saarland University, Saarbrücken, Germany;Mathematical Image Analysis Group Faculty of Mathematics and Computer Science, Saarland University, Saarbrücken, Germany;Departamento de Informática y Sistemas, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;Institut für Informationsverarbeitung, University of Hannover, Hannover, Germany;Max-Planck Institute for Informatics, Saarbrücken, Germany
Venue:
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Year:
2009

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Abstract

We introduce the concept of complementarity between data and smoothness term in modern variational optic flow methods. First we design a sophisticated data term that incorporates HSV colour representation with higher order constancy assumptions, completely separate robust penalisation, and constraint normalisation. Our anisotropic smoothness term reduces smoothing in the data constraint direction instead of the image edge direction, while enforcing a strong filling-in effect orthogonal to it. This allows optimal complementarity between both terms and avoids undesirable interference. The high quality of our complementary optic flow (COF) approach is demonstrated by the current top ranking result at the Middlebury benchmark.