TY - GEN
T1 - Singularity detection from autocovariance via wavelet packets
AU - Victor Wickerhauser, M.
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.
PY - 2001
Y1 - 2001
N2 - We use the eigenvalues of a version of the autocovariance matrix to recognize directions at which the Fourier transform of a function is slowly decreasing, which provides us with a technique to detect singularities in images. In very high dimensions, we show how the wavelet packet best-basis algorithm can be used to compute these eigenvalues approximately, at relatively low computational complexity.
AB - We use the eigenvalues of a version of the autocovariance matrix to recognize directions at which the Fourier transform of a function is slowly decreasing, which provides us with a technique to detect singularities in images. In very high dimensions, we show how the wavelet packet best-basis algorithm can be used to compute these eigenvalues approximately, at relatively low computational complexity.
UR - http://www.scopus.com/inward/record.url?scp=84974528577&partnerID=8YFLogxK
U2 - 10.1007/3-540-45333-4_3
DO - 10.1007/3-540-45333-4_3
M3 - Conference contribution
AN - SCOPUS:84974528577
SN - 9783540453338
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
BT - Wavelet Analysis and its Applications - 2nd International Conference,WAA 2001, Proceedings
A2 - Tang, Yuan Y.
A2 - Yuen, Pong C.
A2 - Li, Chun-hung
A2 - Wickerhauser, Victor
PB - Springer Verlag
T2 - 2nd International Conference on Wavelet Analysis and its Applications, WAA 2001
Y2 - 18 December 2001 through 20 December 2001
ER -