### Table 1: Correspondence of the isometry operator in the spatial and wavelet domains.

1997

Cited by 3

### Table 4: Results for initial fractal block coding in the wavelet domain. Level 2 to Level 1, domain block size 4, 4-block radius search, basis projection not enabled.

"... In PAGE 31: ... Level 2 to Level 1, domain block size 4, no search, basis projection enabled. Table4 the e ects of enabling a 4 block radius search and gure 15 shows the rate distortion curve. Table 5 shows the e ect of the coe cient rate value on a system where the wavelet quantisation rate is held constant ( at 32 in this case), no search and the basis projection enabled.... ..."

### Table 5: Results for initial fractal block coding in the wavelet domain. Level 2 to Level 1, domain block size 4, no search, basis projection enabled.

"... In PAGE 31: ... Table 4 the e ects of enabling a 4 block radius search and gure 15 shows the rate distortion curve. Table5 shows the e ect of the coe cient rate value on a system where the wavelet quantisation rate is held constant ( at 32 in this case), no search and the basis projection enabled. Figure 17 shows the rate/distortion curve for the basic coder with no basis projection, with and without searching enabled.... ..."

### Table 5: An experimental comparison of direct vs. wavelet convolution. The DC columns report direct convolution times, the WC stands for wavelet-domain convolution, the SC stands for wavelet-domain convolution using symmetry. All times are in milliseconds for a single color channel. The WC and SC times include the final reconstruction step, necessary for displaying the convolved result.

Cited by 3

### Table 5: An experimental comparison of direct vs. wavelet convolution. The DC columns report direct convolution times, the WC stands for wavelet-domain convolution, the SC stands for wavelet-domain convolution using symmetry. All times are in milliseconds for a single color channel. The WC and SC times include the final reconstruction step, necessary for displaying the convolved result.

2001

### Table 2: Results for initial fractal block coding in the wavelet domain. Level 2 to Level 1, domain block size 4, no search, basis projection not enabled. Wavelet Rate Value MSE PSNR Bits per Pixel

"... In PAGE 30: ... 5.1 Results Table2 shows the results of using the coder ( with rst range coe cient coder ) with a rate value of 512 and varying the wavelet quantiser rate. Basis projection is not enabled and the coder does no searching for the best range block.... ..."

### Table I. Properties of Rabbit and Dragon Models Used in Multiresolution Experimentsa Model Tetra Face Vertex, n jDomainj jM(l)j MB

2003

Cited by 22

### Table 3: Test results for Expression Classification in terms of average false negative rate (FNR), false positive rate (FPR) and the standard deviations (STD) over 20 iterations. Average False Negative Rates (FNR) Spatial domain (step 1) Wavelet domain (step 2)

2006

"... In PAGE 14: ... 7. Experimental Results In Table3 , we display all the results that have been produced following the two experimental steps (Section 6.4) on the normalized face dataset described in Section 2.... In PAGE 18: ...1. Error rate improvement in relation to the wavelet subspaces My experimental results in Table3 show that D from the D-faces (D, Dx and Dy) of Wavelet AymmetryFaces produces the greatest improvements (86.2% on FNR and 93.... In PAGE 22: ... In this work, we have successfully investigated the implications of wavelet transforms on AsymmetryFaces. We have demonstrated that (1) by applying wavelet transforms on D-faces, a significant improvement can be achieved ( Table3 ); (2) certain subspaces of a wavelet tree have even more discriminative features compared to others, for instance higher frequency band (LH and HL from Table 4) of the wavelet tree; (3) the way S-faces are constructed, their image-intensity domain is already the optimal space with maximum discriminative features. Wavelet transforms are definitely useful at extracting features that can be used to improve classification rates.... ..."

### Table 4.5: Phase ML Gaussian Parameter Estimates and RMS Error in DT Com- plex Wavelet Analysis for Intensity Domain Images

in Speckle Noise Reduction via Homomorphic Elliptical Threshold Rotations in the Complex Wavelet Domain

### Table 4.6: ML Mixed Gaussian Parameter Estimates and RMS Error in Amplitude DT Complex Wavelet Analysis for Intensity Domain Images

in Speckle Noise Reduction via Homomorphic Elliptical Threshold Rotations in the Complex Wavelet Domain