### Table 1 The image model parameters obtained for a two-class [Gray matter, White

1997

### Table 2. Depth complexity values (in total voxel-segments, and maximum/average per view-plane pixel) for rendering of surgical data: grey/white matter from post-contrast SPGR, tumor from T2 weighted MR, and vessels from MRA. Average total storage calculated using 22 bytes per node for a 256A2256 output image.

"... In PAGE 9: ... The numbers for the anatomical parts are for a left view and the bottom two figures are for left and anterior views. Table2 shows the depth complexity of the multi-modality surgical guidance case consisting of 25.6M voxels (equivalent to cubic a data set of size 295 A2 295 A2 295).... ..."

Cited by 1

### Table 2. Depth complexity values (in total voxel-segments, and maximum/average per view-plane pixel) for rendering of surgical data: grey/white matter from post-contrast SPGR, tumor from T2 weighted MR, and vessels from MRA. Average total storage calculated using 22 bytes per node for a 256#02256 output image.

"... In PAGE 9: ... The numbers for the anatomical parts are for a left view and the bottom two figures are for left and anterior views. Table2 shows the depth complexity of the multi-modality surgical guidance case consisting of 25.6M voxels (equivalent to cubic a data set of size 295 #02 295 #02 295).... ..."

Cited by 1

### Table 1. Comparison of our volume measurements with the phantom ground truth. whole brain: total brain tissue(white+gray matter); cortical gray matter1: on the frontal 49 Coronal slices; cortical gray matter2: on the top 56 Axial slices;

1998

"... In PAGE 8: ... Comparison of our volume measurements with the phantom ground truth. whole brain: total brain tissue(white+gray matter); cortical gray matter1: on the frontal 49 Coronal slices; cortical gray matter2: on the top 56 Axial slices; Table1 shows our measurement results over 4 types: total brain tissue (in- cluding white and gray matter), cortical gray matter in selected slices and the white matter. Since the algorithm is designed speci cally for the nearly constant thickness of the cerebral cortex, it recovers only part of the gray matter in the brain stem and the cerebellum where the constant thickness constraint is not satis ed.... ..."

Cited by 10

### Table 1. Comparison of our volume measurements with the phantom ground truth. whole brain: total brain tissue(white+gray matter); cortical gray matter1: on the frontal 49 Coronal slices; cortical gray matter2: on the top 56 Axial slices;

1998

"... In PAGE 8: ... Comparison of our volume measurements with the phantom ground truth. whole brain: total brain tissue(white+gray matter); cortical gray matter1: on the frontal 49 Coronal slices; cortical gray matter2: on the top 56 Axial slices; Table1 shows our measurement results over 4 types: total brain tissue (in- cluding white and gray matter), cortical gray matter in selected slices and the white matter. Since the algorithm is designed speciflcally for the nearly constant thickness of the cerebral cortex, it recovers only part of the gray matter in the brain stem and the cerebellum where the constant thickness constraint is not satisfled.... ..."

Cited by 10

### Table 2 Nonlinear models.

1998

"... In PAGE 16: ... Much of the emphasis will be on the choice of bandwidth and the new aspects brought in by using local polynomial approximation. A power experiment on a wide class of nonlinear models listed in Table2 has been conducted in Section 6.3.... In PAGE 18: ...Table2 , however, where M1(x) is approximately quadratic (see Figure 1), as can be expected the best result is achieved with T = 2 and h = 1. For the ^ L(V1)-tests the size tends to be too low.... In PAGE 18: ... If no corrections are made for this e ect, it will lead to conservative tests. Figure 5 shows the power of the ^ L(V )-tests for model la) of Table2 , and we see the same general trend as for the ^ L(M)-tests; the optimal h increases with T and the derivative. Here ^ L1(V1) also has some power for h = 1 because the variance is constant, not only linear, under the null hypothesis.... In PAGE 18: ... Here ^ L1(V1) also has some power for h = 1 because the variance is constant, not only linear, under the null hypothesis. ^ L0(V1) is much more robust than ^ L0(M1), and this is the case for the other models listed in Table2 as well. 6.... In PAGE 18: ... In particular when we have a nonlinear model, we do of course not want h = 1 to be chosen when T = 0 or T = 1, but with a small autocorrelation, this may well happen for T = 0. In fact h = 1 was chosen in 136 of 500 realizations of model lc) of Table2 which is clearly nonlinear (cf. Figure 1).... In PAGE 19: ... 6.3 A power experiment for a wide set of models We have performed a power experiment for the models listed in Table2 , where t N(0; 0:62) in model ld) - lf), t N(0; 0:72) in lg) - lj) and t N(0; 1) in the other models. Models la) - lj), aa) - ag) and Aa) - Ag) are discussed in Luukkonen et al.... In PAGE 36: ...Figure 1-2: Plots of ^ M1(x) (Figure 1) and ^ V1(e) (Figure 2) for the models listed in Table2 with n = 100 000. The kernel estimator with bandwidth h = 0:2 is used and each plot consists of two realizations.... In PAGE 36: ... The possible values for h is given at the vertical axes. Figure 7: The gure is based on 500 realizations of the models in Table2 . It shows the power of ^ LT (M1) with h cross-validated and n = 100, 250 and 204 for models la) - li), aa) - ag) and Aa) - Ag), respectively.... In PAGE 36: ...ower achieved in Hjellvik and Tj stheim (1995). The nominal size is 0.05. Figure 8: The gure is based on 500 realizations of the models in Table2 and shows the power of ^ LT (V1) with h cross-validated and n = 100, 250 and 204 for models la), aa) - ag) and Aa) - Ag), respectively.... In PAGE 37: ....05 for the standard normal distribution has been used. The model is Xt = t, the bandwidth is h = n?1=9 and the number of realizations are 500. Table2 : Various nonlinear models. Models la) - lj), aa) - ag) and Aa) - Ag) are discussed in Luukkonen et al.... ..."

Cited by 8

### Table 1: The frequencies of surface voxels types in the grey-white matter interface of a segmented 160 200 160 MR brain image.

2003

"... In PAGE 11: ... Here we focus on the presence of voxel types S4 9 in real brain data. Table1 shows the frequency of all nine surface voxel types in a 160 200 160 segmented white matter MR brain image (in the grey-white matter interface). It is seen that all nine voxel classes are represented, and that voxel types S4 9 constitute 3.... In PAGE 21: ... Figure 13: The region of interest on the boundary surface, shown from two viewing points. Table Caption Table1 : The frequencies of surface voxels types in the grey-white matter interface of a segmented 160 200 160 MR brain image.... ..."

Cited by 1

### Table 1. Results: Quantitative comparison of various methods. METHOD CORRECT % PLANES

2007

"... In PAGE 5: ....2. Results and Discussion We performed an extensive evaluation of our algorithm on 588 internet test images, and 134 test images collected using the laser scanner. In Table1 , we compare the following algorithms: (a) Baseline: Both for depth-MRF (Baseline-1) and plane... In PAGE 6: ...) (c) % of models qual- itatively correct, (d) % of major planes correctly identified.3 Table1 shows that both of our models (Point-wise MRF and Plane Parameter MRF) outperform both SCN and HEH in quantitative accuracy in depth prediction. Plane Parame- ter MRF gives better relative depth accuracy, and produces sharper depthmaps.... In PAGE 6: ... (Fig. 7) Table1 also shows that by cap- turing the image properties of connected structure, copla- narity and colinearity, the models produced by the algorithm become significantly better. In addition to reducing quan- titative errors, PP-MRF does indeed produce significantly better 3-d models.... ..."

Cited by 5

### Table 1. Results: Quantitative comparison of various methods. METHOD CORRECT % PLANES

2007

"... In PAGE 5: ....2. Results and Discussion We performed an extensive evaluation of our algorithm on 588 internet test images, and 134 test images collected using the laser scanner. In Table1 , we compare the following algorithms: (a) Baseline: Both for depth-MRF (Baseline-1) and plane... In PAGE 6: ...) (c) % of models qual- itatively correct, (d) % of major planes correctly identified.3 Table1 shows that both of our models (Point-wise MRF and Plane Parameter MRF) outperform both SCN and HEH in quantitative accuracy in depth prediction. Plane Parame- ter MRF gives better relative depth accuracy, and produces sharper depthmaps.... In PAGE 6: ... (Fig. 7) Table1 also shows that by cap- turing the image properties of connected structure, copla- narity and colinearity, the models produced by the algorithm become significantly better. In addition to reducing quan- titative errors, PP-MRF does indeed produce significantly better 3-d models.... ..."

Cited by 5

### Table 5: Possible Implementations of the NL Filter for the NAS-RIF Method Constraint Nonlinear(NL) Filter

"... In PAGE 17: ... In general, a variety of image constraints may be imposed in the nonlinear lter denoted by NL in Figure 8. Table5 gives a list of possibilities. If the image is assumed to be nonnegative with known support, the NL block of Figure 8 represents the projection of the estimated image onto the set of images that are nonnegative with given nite support.... In PAGE 17: ...mposed in the nonlinear lter denoted by NL in Figure 8. Table 5 gives a list of possibilities. If the image is assumed to be nonnegative with known support, the NL block of Figure 8 represents the projection of the estimated image onto the set of images that are nonnegative with given nite support. This requires replacing the negative pixel values within the region of support to zero, and the pixel values outside the region of support to the background grey-level LB as shown in Table5 . Either the nonnegativity constraint or support constraint or both can be used for restoration.... ..."