TY - GEN

T1 - Computing the local continuity order of optical flow using fractional variational method

AU - Kashu, K.

AU - Kameda, Y.

AU - Imiya, A.

AU - Sakai, T.

AU - Mochizuki, Yoshihiko

PY - 2009

Y1 - 2009

N2 - We introduce variational optical flow computation involving priors with fractional order differentiations. Fractional order differentiations are typical tools in signal processing and image analysis. The zero-crossing of a fractional order Laplacian yields better performance for edge detection than the zero-crossing of the usual Laplacian. The order of the differentiation of the prior controls the continuity class of the solution. Therefore, using the square norm of the fractional order differentiation of optical flow field as the prior, we develop a method to estimate the local continuity order of the optical flow field at each point. The method detects the optimal continuity order of optical flow and corresponding optical flow vector at each point. Numerical results show that the Horn-Schunck type prior involving the n + ε order differentiation for 0 < ε < 1 and an integer n is suitable for accurate optical flow computation.

AB - We introduce variational optical flow computation involving priors with fractional order differentiations. Fractional order differentiations are typical tools in signal processing and image analysis. The zero-crossing of a fractional order Laplacian yields better performance for edge detection than the zero-crossing of the usual Laplacian. The order of the differentiation of the prior controls the continuity class of the solution. Therefore, using the square norm of the fractional order differentiation of optical flow field as the prior, we develop a method to estimate the local continuity order of the optical flow field at each point. The method detects the optimal continuity order of optical flow and corresponding optical flow vector at each point. Numerical results show that the Horn-Schunck type prior involving the n + ε order differentiation for 0 < ε < 1 and an integer n is suitable for accurate optical flow computation.

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U2 - 10.1007/978-3-642-03641-5_12

DO - 10.1007/978-3-642-03641-5_12

M3 - Conference contribution

AN - SCOPUS:70350608278

SN - 3642036406

SN - 9783642036408

VL - 5681 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 154

EP - 167

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2009

Y2 - 24 August 2009 through 27 August 2009

ER -