Share Email Print

Proceedings Paper

Ripplet-II transform for feature extraction
Author(s): Jun Xu; Dapeng Wu
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

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.

Paper Details

Date Published: 4 August 2010
PDF: 10 pages
Proc. SPIE 7744, Visual Communications and Image Processing 2010, 77441R (4 August 2010); doi: 10.1117/12.863013
Show Author Affiliations
Jun Xu, Univ. of Florida (United States)
Dapeng Wu, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 7744:
Visual Communications and Image Processing 2010
Pascal Frossard; Houqiang Li; Feng Wu; Bernd Girod; Shipeng Li; Guo Wei, Editor(s)

© SPIE. Terms of Use
Back to Top