### Table 1 shows a summary of statistics for the experi- ment. As can be seen in both the graphs and the table, the new formulation converges faster to the planar structure and camera parameter estimates. In addition, the new for- mulation tends to be more stable as noise levels increase; the mean error and variance in estimating the plane normal and the camera parameters are both smaller when the new formulation is employed.

2000

"... In PAGE 5: ...2366 0.4673 gt;100 19 Table1 : Average performance statistics for synthetic data exper- iments with increasing noise level. Experiments were conducted in trials with varying uniform noise (standard deviation 2, 6, and 10 pixels).... ..."

Cited by 10

### Table 1 shows a summary of statistics for the experi- ment. As can be seen in both the graphs and the table, the new formulation converges faster to the planar structure and camera parameter estimates. In addition, the new for- mulation tends to be more stable as noise levels increase; the mean error and variance in estimating the plane normal and the camera parameters are both smaller when the new formulation is employed.

2000

"... In PAGE 5: ...2366 0.4673 a176 100 19 Table1 : Average performance statistics for synthetic data exper- iments with increasing noise level. Experiments were conducted in trials with varying uniform noise (standard deviation 2, 6, and 10 pixels).... ..."

Cited by 10

### Table 1. The planar Ramsey numbers for cycles

"... In PAGE 8: ... Remarks Remark 1. Some theorems presented in Table1 can be formulated... ..."

### Table 1 summarizes the current status of the newly formulated problem of colored simultaneous embedding. A check indicates that it is always possible to simultaneously embed the type of graphs, a a23 indicates that it is not always possible, and a ? indicates an open problem.

2007

"... In PAGE 11: ... Table1 . k-colored simultaneous embeddings: results and open problems.... ..."

Cited by 3

### Table 1 summarizes the current status of the newly formulated problem of colored simultaneous embedding. A check indicates that it is always possible to simultaneously embed the type of graphs, a a23 indicates that it is not always possible, and a ? indicates an open problem.

2007

"... In PAGE 11: ... Table1 . k-colored simultaneous embeddings: results and open problems.... ..."

Cited by 3

### Table 27: Planarity, P

1996

### Table 1: Average performance statistics for synthetic data exper- iments with increasing noise level. Experiments were conducted in trials with varying uniform noise (standard deviation 2, 6, and 10 pixels). Mean error and root mean squared error are shown for the recovered camera motion parameters (translation, rotation). For the static parameters (structure and camera) the table provides the frame number for which the camera parameters converge to within 5% of the true value, and frame number for which the nor- mal converges to within 0:5 o of its true value.

2000

"... In PAGE 3: ... To avoid clutter in the graphs we do not show t Y , q 1 , q 2 , and N Z . Table1 shows a summary of statistics for the experi- ment. As can be seen in both the graphs and the table, the new formulation converges faster to the planar structure and camera parameter estimates.... ..."

Cited by 10

### Table 1: Average performance statistics for synthetic data exper- iments with increasing noise level. Experiments were conducted in trials with varying uniform noise (standard deviation 2, 6, and 10 pixels). Mean error and root mean squared error are shown for the recovered camera motion parameters (translation, rotation). For the static parameters (structure and camera) the table provides the frame number for which the camera parameters converge to within 5% of the true value, and frame number for which the nor- mal converges to within 0:5o of its true value.

2000

"... In PAGE 3: ... To avoid clutter in the graphs we do not show tY , q1, q2, and NZ. Table1 shows a summary of statistics for the experi- ment. As can be seen in both the graphs and the table, the new formulation converges faster to the planar structure and camera parameter estimates.... ..."

Cited by 10

### Table 8: E ciency ratios for LWP over LBP, RLWP, and the default path, for triangle graphs with di erent numbers of task repetitions (n = 100, = 4). graphs is more di cult, because true shortcuts (ones that bypass large portions of the graph) do not exist. This means that the interleaved algorithms apos; essentially local nature may preclude them from nding some good paths, while LBP must search much longer to nd a short path in its initial exploration phase. Since = 4 gave the best performance for LBP and RLWP, we present results for that value, and thus conservatively estimate the relative usefulness of LWP. It appears that LWP is never much worse than LBP and RLWP for triangle graphs, and is sometimes much better. The di erence is most pronounced in comparison with LBP, where we nd a considerable gain in e ciency in using LWP over LBP as the size of the environment increases (see Table 7). We also saw such a trend for random graphs, though the e ect was much less. The di erence between the two interleaved algorithms, LWP and RLWP, is less pronounced, however; using a utility-based formulation instead of random local exploration may not help much in some planar environments.

1999

Cited by 1