### Table 1. Robust estimation of the epipolar geometry from a set of matches containing outliers using RANSAC (a61 OK indicates the probability that the epipolar geometry has been correctly estimated).

"... In PAGE 4: ...a41a48a44 Our system incorporates the RANSAC (RANdom SAmpling Consesus)a96 approach used by Torr et al.a41a43a96 Table1 sketches this technique. Once the epipolar geometry has been retrieved, one can start looking for more matches to re ne this geometry.... In PAGE 5: ...his is illustrated in Fig. 3. The rst steps consists of nding the epipolar geometry as described in Section 3.1. Then the matches which correspond to already reconstructed points are used to compute the projection matrix a106 a100 . This is done using a robust procedure similar to the one laid out in Table1 . In this case a minimal sample of 6 matches is needed to compute a106 a100 .... ..."

### Table 1. Robust estimation of the epipolar geometry from a set of matches containing outliers using RANSAC (a61 OK indicates the probability that the epipolar geometry has been correctly estimated).

"... In PAGE 4: ...a41a48a44 Our system incorporates the RANSAC (RANdom SAmpling Consesus)a95 approach used by Torr et al.a41a43a95 Table1 sketches this technique. Once the epipolar geometry has been retrieved, one can start looking for more matches to refine this geometry.... In PAGE 5: ...his is illustrated in Fig. 3. The first steps consists of finding the epipolar geometry as described in Section 3.1. Then the matches which correspond to already reconstructed points are used to compute the projection matrix a104 a99 . This is done using a robust procedure similar to the one laid out in Table1 . In this case a minimal sample of 6 matches is needed to compute a104 a99 .... ..."

### Table 1. Robust estimation of the epipolar geometry from a set of matches con- taining outliers using RANSAC (POK indicates the probability that the epipolar geometry has been correctly estimated).

1998

"... In PAGE 6: ...Table1... In PAGE 7: ...ion 3.1. Then the matches which correspond to already reconstructed points are used to compute the projection matrix Pk. This is done using a robust pro- cedure similar to the one laid out in Table1 . In this case a minimal sample of 6 matches is needed to compute Pk.... ..."

Cited by 14

### Table 1: Temporal signatures

2004

"... In PAGE 3: ... We first identify verb complexes including modals and auxiliaries and then classify tensed and non-tensed expressions along the following dimensions: finiteness, non-finiteness, modality, aspect, voice, and polarity. The values of these features are shown in Table1 . The features finiteness and non-finiteness are mutually exclusive.... ..."

Cited by 12

### Table 2: Temporal signatures

2006

"... In PAGE 11: ... We rst identify verb complexes including modals and auxiliaries and then classify tensed and non-tensed expressions along the following dimensions: niteness, non- niteness, modality, aspect, voice, and polarity. The values of these features are shown in Table2 . The features niteness and non- niteness are mutually exclusive.... ..."

Cited by 2

### Table 2: Temporal signatures

2006

"... In PAGE 11: ... We first identify verb complexes including modals and auxiliaries and then classify tensed and non-tensed expressions along the following dimensions: finiteness, non-finiteness, modality, aspect, voice, and polarity. The values of these features are shown in Table2 . The features finiteness and non-finiteness are mutually exclusive.... ..."

Cited by 2