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.
UR - http://www.scopus.com/inward/record.url?scp=70350608278&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350608278&partnerID=8YFLogxK
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 -