### Table 1: Model-based segmentation and visualization

"... In PAGE 8: ... As a simple example, colors and transparencies of such objects should be selected such that contrasts are easily perceived and all relevant objects are sufficiently visible. Comparison of Model-based Segmentation and Visualization In Table1 , we compare information used for model-based segmentation and visualization. Table 1: Model-based segmentation and visualization... ..."

### Table 2. Optimal views based on model-based reconstruction.

2004

"... In PAGE 5: ... Based on an average reconstruction time of 30 sec- onds, this search takes about 45 hours. The results are presented in Table2 which shows the optimal views for K = {1,2,3,4,5} and the correspond- ing minimum average reconstruction errors (refer to Table 1 Figure 7. Reconstruction errors for all view con- figurations with 4 cameras (K = 4) ranked by magnitude of ensemble error.... In PAGE 5: ... Figure 7 shows the errors of all combinatorial view configurations for the case K = 4, ranked in ascending order of error. Each er- ror bar represents the subjects standard deviation for that configuration (the first error bar corresponds to the optimal configuration and is the subject standard deviation listed in Table2 ). Other plots for K = 1,2,3 and 5 are quite sim- ilar in nature, all showing a well-defined minimum with the subject variation (error-bars) being lowest for the best configuration (left most) and highest for the worst (right most).... In PAGE 5: ... Using the same search strategy, we now evaluate the visual hull constructions ob- tained from the given subset of silhouette images and com- pare them to the ground truth. Table 3 shows the optimal views for K = {2,3,4,5} and the corresponding error val- ues (same format as in Table2 except that the visual hull from a single silhouette (K = 1) has no finite volume and... In PAGE 6: ... There are a few dif- ferences but these are somewhat misleading. The best view configurations in Table2 are marked in Figure 8 with ar- rows. We note that our model-based optimal views have al- most the same errors as the best views chosen with visual hull method and are always in the first plateau or top quar- tile that includes the key profile view #10.... ..."

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### Table 2. Optimal views based on model-based reconstruction.

2004

"... In PAGE 7: ... Based on an average reconstruction time of 30 seconds, this search takes about 45 hours. The results are presented in Table2 which shows the optimal views for K = {1,2,3,4,5} and the correspond- ing minimum average reconstruction errors (refer to Table 1 for exact coordinates). The standard deviation of the indi- Figure 7.... In PAGE 7: ... Figure 7 shows the errors of all combinatorial view configurations for the case K = 4, ranked in ascending order of error. Each er- ror bar represents the subjects standard deviation for that configuration (the first error bar corresponds to the optimal configuration and is the subject standard deviation listed in Table2 ). Other plots for K = 1,2,3 and 5 are quite sim- ilar in nature, all showing a well-defined minimum with the subject variation (error-bars) being lowest for the best configuration (left most) and highest for the worst (right most).... In PAGE 7: ... Using the same search strategy, we now evaluate the visual hull construc- tions obtained from the given subset of silhouette im- ages and compare them to the ground truth. Table 3 shows the optimal views for K = {2,3,4,5} and the correspond- ing error values (same format as in Table2 except that the visual hull from a single silhouette (K = 1) has no fi- nite volume and is omitted). Note that a visual hull recon- struction (especially one from few images) is not a very... In PAGE 8: ... Interestingly, the first plateau corresponding to the top group is all the sub- sets which include the profile view #10 (one of the most salient). We can see marked similarities in the opti- mal views in Table2 and Table 3. For example, both methods indicate views #3 and #10 to be the most infor- mative.... In PAGE 8: ... For example, the two most salient views (#3 and #10) correspond very closely with the established (biomet- ric) standards of 3/4 view (INS photos) and profile view ( mugshot photos). We have not yet searched for K gt; 5 mainly due to the computational costs, but it appears that reconstructions do not improve significantly beyond 4-5 views (see the best errors listed in Table2 ). One can easily incorporate additional physical and operational constraints into our framework.... ..."

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### Table 2. Experiments with the self-localization algorithms (90-dergee agent viewing sector)

"... In PAGE 12: ... This is consistent with the reported gain from using the particle filter [2]. It is safe to say that Table2 contains the minimal achievable average self-localization errors in the simulated soccer and could be used as the reference point. In a separate set of the experiments we assessed the benefits of two-agent ball tracking.... ..."

### Table3. Result of a self-localization I ComuutedValue I Truevalue I

"... In PAGE 4: ...070950.5). Using the extracted four image points 1, 2, 5, 6 and (x,,, yn,) with the corresponding object points, we can estimate the object coordinates of (xn,, y,, ) that corresponds to the position of the robot in the global coordinate system by relative positioning. Table3 presents the estimation result by the proposed method. Table3.... ..."

### Table 4: Comparison with model-based clustering using the EM algorithm.

1997

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### Table 9 Comparison of Actual and Model-Based GRPs

"... In PAGE 12: ... We caution the readers that the solution may not be optimal but provides a better allocation. One such allocation is reported in Table9 and depicted in Figure 5. The horizontal bar graph com- pares the improved and the actual allocation of resources (measured in total GRPs) across the dif- ferent themes.... In PAGE 12: ... Our model suggests increasing adver- tising expenditure on reconnect and reassurance ads while decreasing on the other three themes. The per- centage changes in allocation of GRPs are quite large as seen in Table9 . They range from a low of 50% to a high of 276% change.... ..."

### Table 2: Summary of predictive model-based schemes described.

1998

"... In PAGE 13: ... In addition, iterative schemes must be used to estimate the multiple components once the corrupted-speech observations have been generated. 6 Summary of Techniques Table2 goes here The techniques presented in the previous sections are summarised in table 2. The table gives; the technique, the nal form of the corrupted-speech model for each speech and noise pairing, the domain it is normally applied in, the features that may be compensated, the model parameters that may be compensated, and the computational cost of the technique13.... ..."

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### Table 4 presents the result of self-localization when Y,= *.

"... In PAGE 4: ...Table 4 presents the result of self-localization when Y,= *. Table4 . Result of a self-localization when Y, = 00 I Comuuted Value I Truevalue I I (-145437.... ..."

### Table 1: Minimal Solutions for Sensor Self-Localization

2003

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