### Table 3 Depth measurements for points labeled in Fig. 11

"... In PAGE 11: ... Figure 11 shows a sample image from the camera head and the depth map generated by the hardware. In Table3 , the depth estimates obtained by our system are compared with hand-measured depths. Since the hard- ware outputs disparity, and not depth, estimates, depths were computed according to d = fT D Fig.... In PAGE 11: ... Since the hard- ware outputs disparity, and not depth, estimates, depths were computed according to d = fT D Fig. 11 This figure compares the estimates of the hardware implementation against hand-measured ground truth shown in Table3 .Ina is shown one of the original images from the pair used to generate the disparity map shown in b where d is depth, f is the focal length of the imaging system, T is the baseline between the cameras, and D is the disparity.... In PAGE 11: ...xample is the depth estimate for point 3 in Fig. 11a). This point lies on an occlusion boundary (where local al- gorithms are expected to do poorly, [24]), and inspection of the disparity map (b) shows that it is obviously in er- ror. It is worth noting that despite the good results shown in Table3... ..."

### Table 1: Lattice points and labels for a Voronoi set of the product sublattice as labeled in Figure 4.

2000

"... In PAGE 15: ... Each point carries a pair of labels ( 1; 2) with 1 2 1, 2 2 2. The complete listing of the labeling is given in Table1 . In this example we have set 1 = 9 and 2 = 5, which determines the respective distortions di obtained by the design.... ..."

Cited by 22

### Table 1: Lattice points and labels for a Voronoi set of the product sublattice as labeled in Figure 4.

"... In PAGE 15: ... Each point carries a pair of labels ( 1; 2) with 1 2 1, 2 2 2. The complete listing of the labeling is given in Table1 . In this example we have set 1 = 9 and 2 = 5, which determines the respective distortions di obtained by the design.... ..."

### Table 1: Location Error Evaluation #1: The principal component standard deviations for the predicted error covariance and the sample covariances derived from the ^sML estimates. Point labels refer to source locations in Figure 3.

1995

"... In PAGE 15: ... The lines in Figure 3b display the scaled principal component vectors which correspond to the axes of the hyper-ellipsoid associated with the predicted error covariance of each source-location shown in the Figure 3a. Table1 presents detailed numerical data for selected source-locations in Figure 3. In each case, the principal component standard deviations are listed for the predicted error covariance as well as for the sample covariances associated with the set of ^sML estimates.... In PAGE 15: ... This value is calculated as the square root of the component variance summation and is equivalent to the square root of the trace of the covariance matrix. Several characteristics are apparent from Figure 3 and Table1 . The predicted error covariance is generally within a few centimeters of the observed covariance of the ^sML estimator.... ..."

Cited by 1

### Table 2: Location Error Evaluation #2: The principal component standard deviations for the predicted error covariance and the sample covariances derived from the ^sML estimates. Point labels refer to source-locations in Figure 4.

1995

"... In PAGE 18: ... Now that the array con guration is symmetric in two dimensions, the source-locations in each of the four parallel quarter-planes are at a single height, ranging from the midpoint of the room, 2m, to a half-meter short of the ceiling, 3:5m. The results of this experiment are presented in Figure 4 and Table2 . Once again, the expression (16) accurately predicts the results of the Monte Carlo simulations.... ..."

Cited by 1

### Table 1: Accuracy and CPU time in seconds for the linear programming formulation [25] and the proposed proximal formulation. Each running time result is the total time needed to set up and solve the optimization problem, either as a linear program or a linear system of equations, 225 times. The time ratio is the time for the linear programming formulation divided by the time for the proximal formulation.

"... In PAGE 5: ... The checkerboard dataset consists of points with labels black and white arranged in the shape of a checkerboard, while the spiral dataset consists of points from two concentric spirals. Table1 shows both the accuracy and CPU time needed to run the experiments of [25] using both the linear programming formulation originally used in [25] and our proposed proximal formulation. Both experiments were carried out using the procedures and prior knowledge described in [25].... ..."

### Table 3: Values of a feature of type I2 used to the identify the tank, for correct and mistaken hypothesis. The feature consisted of point set f3; 5; 7; 9g, corresponding to the points labeled in gure 4. The feature value is formed using the thermophysical model of tank and the data from the respective other vehicles. When this feature is applied to the correctly hypothesized data of the tank it has a mean value of 2.52 and a standard deviation of 1.82. The feature value under a mistaken hypothesis is at least 3.3 standard deviations away from the average value under the correct hypothesis.

"... In PAGE 23: ... Table 2 shows similar results for a feature of type I2 also used for separating truck 1 from other objects. Table3 describes the performance of a feature of type I2 used to separate the tank object class from other classes. Features for each of the other object classes were also examined, and performance similar to that described above was observed.... ..."

### Table 2: Technology Mapping results

"... In PAGE 8: ... The results show that the Boolean approach reduces the number of matching algorithm calls, nd smaller area circuits in better CPU time, and reduces the initial network graph because generic 2-input base function are used. Table2 presents a comparison between SIS and Land for the library 44-2.genlib, which is distributed with the SIS package.... ..."

### Table 3b. Solution Statistics for Model 2 (Minimization)

1999

"... In PAGE 4: ...6 Table 2. Problem Statistics Model 1 Model 2 Pt Rows Cols 0/1 Vars Rows Cols 0/1 Vars 1 4398 4568 4568 4398 4568 170 2 4546 4738 4738 4546 4738 192 3 3030 3128 3128 3030 3128 98 4 2774 2921 2921 2774 2921 147 5 5732 5957 5957 5732 5957 225 6 5728 5978 5978 5728 5978 250 7 2538 2658 2658 2538 2658 120 8 3506 3695 3695 3506 3695 189 9 2616 2777 2777 2616 2777 161 10 1680 1758 1758 1680 1758 78 11 5628 5848 5848 5628 5848 220 12 3484 3644 3644 3484 3644 160 13 3700 3833 3833 3700 3833 133 14 4220 4436 4436 4220 4436 216 15 2234 2330 2330 2234 2330 96 16 3823 3949 3949 3823 3949 126 17 4222 4362 4362 4222 4362 140 18 2612 2747 2747 2612 2747 135 19 2400 2484 2484 2400 2484 84 20 2298 2406 2406 2298 2406 108 Table3 a. Solution Statistics for Model 1 (Maximization) Pt Initial First Heuristic Best Best LP Obj.... In PAGE 5: ...) list the elapsed time when the heuristic procedure is first called and the objective value corresponding to the feasible integer solution returned by the heuristic. For Table3 a, the columns Best LP Obj. and Best IP Obj.... In PAGE 5: ... report, respectively, the LP objective bound corresponding to the best node in the remaining branch-and-bound tree and the incumbent objective value corresponding to the best integer feasible solution upon termination of the solution process (10,000 CPU seconds). In Table3 b, the columns Optimal IP Obj., bb nodes, and Elapsed Time report, respectively, the optimal IP objective value, the total number of branch-and-bound tree nodes solved, and the total elapsed time for the solution process.... ..."

### TABLE l Parameter values fitted by the optimization algorithms for the test problem*

1976