TY - GEN
T1 - Ripplet-II transform for feature extraction
AU - Xu, Jun
AU - Wu, Dapeng
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2010
Y1 - 2010
N2 - Current image representation schemes have limited capability of representing 2D singularities (e.g., edges in an image). Wavelet transform has better performance in representing 1D singularities than Fourier transform. Recently invented ridgelet and curvelet transform achieve better performance in resolving 2D singularities than wavelet transform. To further improve the capability of representing 2D singularities, this paper proposes a new transform called ripplet transform Type II (ripplet-II). The new transform is able to capture 2D singularities along a family of curves in images. In fact, ridgelet transform is a special case of ripplet-II transform with degree 1. Ripplet-II transform can be used for feature extraction due to its efficiency in representing edges and textures. Experiments in texture classification and image retrieval demonstrate that the ripplet-II transform based scheme outperforms wavelet and ridgelet transform based approaches. © 2010 SPIE.
AB - Current image representation schemes have limited capability of representing 2D singularities (e.g., edges in an image). Wavelet transform has better performance in representing 1D singularities than Fourier transform. Recently invented ridgelet and curvelet transform achieve better performance in resolving 2D singularities than wavelet transform. To further improve the capability of representing 2D singularities, this paper proposes a new transform called ripplet transform Type II (ripplet-II). The new transform is able to capture 2D singularities along a family of curves in images. In fact, ridgelet transform is a special case of ripplet-II transform with degree 1. Ripplet-II transform can be used for feature extraction due to its efficiency in representing edges and textures. Experiments in texture classification and image retrieval demonstrate that the ripplet-II transform based scheme outperforms wavelet and ridgelet transform based approaches. © 2010 SPIE.
KW - Image retrieval
KW - Radon transform
KW - Ridgelet transform
KW - Texture classification
KW - Wavelet transform
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-78649797860&origin=recordpage
U2 - 10.1117/12.863013
DO - 10.1117/12.863013
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9780819482341
VL - 7744
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Visual Communications and Image Processing 2010
T2 - Visual Communications and Image Processing 2010
Y2 - 11 July 2010 through 14 July 2010
ER -