### Table 1. Pairs of polytopes of different dimensions embedded in 1D, 2D, and 3D space. If their relative configuration allows them to be embedded in a lower-dimensional linear subspace, a degeneracy arises (simple, if the difference between the dimension of the ambient space and that of the subspace is one, and double, if this difference is two)

"... In PAGE 2: ... Algorithms for intersection detection between poly- hedra are based on interference tests between lower- dimensional entities. Table1 lists all possible pairs of polytopes embedded in the 1D, 2D, and 3D Eu- clidean space, together with references to the tests proposed for the corresponding intersection relation. Depending on the application, particularities of the involved polytopes can be exploited to attain some degree of simplicity or efficiency.... ..."

### Table 1. Representation of linear subspaces of P3.

### Table 5. 3-D transport benchmark results

2000

"... In PAGE 16: ... Konno [9] also calculated the total flux using MCNP4B2, and his results agree well with the present results within statistical errors. Benchmark results As shown in Table5 , there are eight contributions for the present benchmarks. Six contributions were obtained by using discrete ordinates method programs.... ..."

### Table 1. Information content of the different plenoptic subspaces with regard to the 3D motion estimation prob- lem

2003

"... In PAGE 6: ... 1b). We collected the motion constraint equations for all the plenoptic subspaces in Table1 , and we see that the cam- era that makes the motion estimation problem the easiest is the one that samples the whole plenoptic function (or a multi-perspective 3D slice of it for the case of planar mo- tion) because the motion estimation problem is reduced to a low-dimensional image registration problem as said before. Another important criteria is the range of directions (field of view) of the sensor.... In PAGE 6: ... To simplify the exposition we as- sume that the robot is only able to move on a planar, flat surface, thus the locomotion of the robot is limited to a hor- izontal planar motion, and that the camera designs under study are restricted sets of horizontally aligned pinhole line cameras. As summarized in Table1 in the previous section, we can see that we can extract the 3 planar motion param-... ..."

Cited by 4

### Table 1. Information content of the different plenoptic subspaces with regard to the 3D motion estimation prob- lem

2003

"... In PAGE 6: ... 1b). We collected the motion constraint equations for all the plenoptic subspaces in Table1 , and we see that the cam- era that makes the motion estimation problem the easiest is the one that samples the whole plenoptic function (or a multi-perspective 3D slice of it for the case of planar mo- tion) because the motion estimation problem is reduced to a low-dimensional image registration problem as said before. Another important criteria is the range of directions (field of view) of the sensor.... In PAGE 6: ... To simplify the exposition we as- sume that the robot is only able to move on a planar, flat surface, thus the locomotion of the robot is limited to a hor- izontal planar motion, and that the camera designs under study are restricted sets of horizontally aligned pinhole line cameras. As summarized in Table1 in the previous section, we can see that we can extract the 3 planar motion param- eters directly from the image data if we are able to capture Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV 2003) 2-Volume Set ... ..."

Cited by 4

### Table 2. Covariance for translation ^ Ct in the linear subspace method. n Theoretical Bootstrap EIF

1998

Cited by 5

### Table 3: Sufficient subspaces (BTCX) under each of CUBDBN BM BM BM BN CUBJ

"... In PAGE 12: ... It demands that for each CUCX, we find: B4BT A0 D7D9D4D4D3D6D8B4CUCXB5B5 CJ CUCXB4D7D9D4D4D3D6D8B4CUCXB5B5 We denote such a set induced by CUCX as BTCX. Using the above-described supports and the sets they are mapped to, Table3 presents these in regular expression form for each of CUBD through CUBJ. In the first two, we present this set first in explicit terms, then in terms of its negation removed from BT; subsequent sets are presented only in terms of the... ..."

### Table 3: Sufficient subspaces (BT CX ) under each of CU BD BNBMBMBMBNCU BJ

"... In PAGE 12: ... It demands that for each CU CX , we find: B4BTA0D7D9D4D4D3D6D8B4CU CX B5B5 CJ CU CX B4D7D9D4D4D3D6D8B4CU CX B5B5 We denote such a set induced by CU CX as BT CX . Using the above-described supports and the sets they are mapped to, Table3 presents these in regular expression form for each of CU BD through CU BJ . In the first two, we present this set first in explicit terms, then in terms of its negation removed from BT; subsequent sets are presented only in terms of the... ..."

### Table 3. Rendering Time Per Frame (Sec- onds): GSO - Gouraud Shading Only, 3DTMN - 3D Texture Mapping (Nearest), 3DTML - 3D Texture Mapping (Linear)

"... In PAGE 6: ... The timings were measured on an SGI Octane workstation with a 195 MHz R10000 CPU and 256 Mbytes of memory without hardware graphics ac- celeration. Table3 reports the average time per frame in seconds for three difference rendering modes. The GSO field in this table is the time taken for rendering the objects using Gouraud shading only, and indicates how complicated is the involved rendering.... ..."

### Table 2: First-stage multi-resolution subspace analysis results evaluated under difierent p.

"... In PAGE 31: ... Red curve with o: evaluated on p = 1. the Gaussian kernel, as shown in Table2 , is 90:2%. Similarly, the overall accuracy for using linear kernel for KDA is 92:3%, which is only slightly worse then KDA with Gaussian kernel, which is 94:0%.... ..."