### Table 1. Quantitative analysis of reconstruc- tion errors

2001

"... In PAGE 7: ... The second set was generated by applying weights, as explained in the previous section, with rescaling factors AD BC BP BDBMBC and AD BD BP BDBCBMBC that are chosen empirically through the analysis of distribution of wavelets coefficients. Statistics on our objective fidelity cri- teria are summarized in Table1 (a) for the two cases. The root mean squared error (RMSE) is the square root of the average of the squared error measure, and it is one of the most often used average measure.... In PAGE 8: ... Figure 8(i) shows the 270th coronal slice colored according to the maximally combined weights. 23% of vox- els have positive weights, and the reconstruction errors over these voxels are compared in Table1 (b). From the through examination of visualized images, we find that the patterns of performance in visualization for both the Visible Man data and the Bighead data are very similar to each other.... ..."

Cited by 9

### Table 2 Average depth of pseudo trees vs. DFS trees; 100 instances of each random model Model (DAG) width Pseudo tree depth DFS tree depth

"... In PAGE 18: ... Therefore, Theorem 38 A graphical model that has a treewidth DB A3 has an AND/OR search tree whose size is C7B4D2 A1 CZ B4DB A3 A1D0D3CV D2B5 B5, where CZ bounds the domain size and D2 is the number of variables. For illustration, Table2 shows the effect of DFS spanning trees against pseudo trees, both generated using brute-force heuristics over randomly generated graphs, where C6 is the number of variables, C8 is the number of variables in the scope of a function and BV is the number of functions. 4.... ..."

### Table 4. Comparison of surface reconstruc- tion techniques from unorganized points.

in Multi-Scale Reconstruction of Implicit Surfaces with Attributes from Large Unorganized Point Sets

2004

Cited by 1

### Table 2. Bunny (35 947 points). Reconstruc- tion time (in seconds) with respect to Tleaf , q

in Multi-Scale Reconstruction of Implicit Surfaces with Attributes from Large Unorganized Point Sets

2004

"... In PAGE 8: ...ize q on the reconstruction quality. Tleaf BP 50. sen continuity, but the surface appears not pleasant at over- lapping zones. Table2 shows the quantitative results of the Stanford Bunny reconstruction with different parameters for Tleaf and q. Note that high values for Tleaf increase the reconstruction time significantly.... ..."

Cited by 1

### Table 4: Mean Square Error (MSE) and computation time for coding and reconstruc- tion of piano and speech signals with compression corresponding to D = 3 for various sizes of data frame. Coifman 12 wavelets were used.

1997

"... In PAGE 26: ... It is thus to be expected that better matches between domain and range blocks, and correspondingly lower error values, should be obtained. Table4 indicates the mean square errors and computation times associated... ..."

Cited by 3

### TABLE IV. Kinematic properties of dilepton events (momenta in GeV) used in the reconstruc- tion of the top quark mass. All corrections are included. Event Object

### Table 2: Timing results in seconds on the CM-5 for di erent implementations of the Pasciak algorithm. The three major steps in the algorithm are shown, were the ltering step is combined with that of the two-dimensional inverse Fourier transform. The last column shows the total time needed for reconstruc- tion.

"... In PAGE 7: ... The parameter N has the same meaning. The results are shown in Table2 for di erent values of N. In the interpolation step there is some communica- tion between processors when at a certain point the actual interpolation is computed.... In PAGE 8: ... The last column shows the total time needed for reconstruc- tion. When the timing results in Table2 are compared to the timings for ltered backprojection (Table 1), it can be concluded that the Pasciak method is indeed faster than ltered backprojection. 5 Conclusions Of the reconstruction algorithms considered, the l- tered backprojection algorithm turns out to be the most di cult to parallelise.... ..."

### Table 1: Comparison with three scenes, PT : photon tracing, NS : number of splats, PS1 : photon splatting for a 512x512 image and PS2 : photon splatting for a 900x900 image , IR : image reconstruc- tion

### Table 2. Intrinsic parameters found from the images shown in fig. 3. The variation of the fo- cal length is compatible with the zooming and focusing suggested by the motion, in partic- ular the zooming out of the fourth view. The principal point varies quite unpredictably, but this behaviour does not affect the reconstruc- tion.

### Table 1. Bayesian Networks Repository (left); SPOT5 benchmarks (right).

2005

"... In PAGE 9: ... The result of this process is a tree of hypergraph separators which is also a pseudo-tree of the original model since each separator corresponds to a subset of variables chained together. In Table1 we computed the height of the pseudo-tree obtained with the hypergraph and minfill heuristics for 10 belief networks from the UAI Repository2 and 10 constraint networks derived from the SPOT5 benchmark [17]. For each pseudo-tree we also com- puted the induced width of the elimination order obtained from the depth-first traversal of the tree.... ..."

Cited by 2