The redundant discrete wavelet transform and additive noise
By James E. Fowler. 2005
368 citations recorded by the source corpus.
Subfields: Applied Mathematics, Computer Vision and Pattern Recognition, Control and Systems Engineering, Speech & Audio
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Mathematics
In Computer Vision and Pattern Recognition
- Alan L. Yuille
- Luc Van Gool
- Liang-Chieh Chen
- Iasonas Kokkinos
- Ming-Hsuan Yang
- Dacheng Tao
- Xiaogang Wang
- Trevor Darrell
In Control and Systems Engineering
- Iasonas Kokkinos
- Alan L. Yuille
- Kevin P. Murphy
- Richa Singh
- Mayank Vatsa
- Afzel Noore
- Richard Zhang
- Max M. Houck
In Speech & Audio
- Alan L. Yuille
- Kevin P. Murphy
- Iasonas Kokkinos
- Richa Singh
- Mayank Vatsa
- Afzel Noore
- George Papandreou
- Richard Zhang
Citations in the corpus
- Making Convolutional Networks Shift-Invariant Again (2019, background): “Shift-equivariance can be recovered if features are extracted densely, for example tex-tons (Leung & Malik, 2001), the Stationary Wavelet Trans-form (Fowler, 2005), and DenseSIFT (Vedaldi & Fulkerson, 2008).”
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs (2016, background): “We refer the interested reader to [74] for early references from the wavelet literature.”
- Quality-augmented fusion of level-2 and level-3 fingerprint information using DSm theory (2009, positive): “(1) represents the l level decomposition of image I ½ I Aj ; I Hj ; I Vj ; I Dj ¼ RDWT ð I Þ ð 1 Þ where j = 1,2, . . . , l represents the level of decomposition and i = A , H , V , D denotes the approximation, horizontal, vertical, and diagonal subbands at l levels of decomposition.”
- MAKING CONVOLUTIONAL NETWORKS SHIFT-INVARIANT AGAIN (2018, background): “Shiftequivariance can be recovered if features are extracted densely, for example textons (Leung & Malik, 2001), the Stationary Wavelet Transform (Fowler, 2005), and DenseSIFT (Vedaldi & Fulkerson, 2010).”