### Table 3: The computed for each pair of conics Ci and C0 j, Ci is a conic of the rst image and C0 j a conic of the second image.

1995

"... In PAGE 16: ... The closeness of the set of the conics generated in the second image can be deduced from Figure 3 in which four of them are displayed. Table3 shows the computed for each pair of conics. The absolute value of increases with the increasing discrepancy of conic pairs.... ..."

Cited by 4

### Table 2: This table shows all the conic/line-pair invariants extracted from the calibration images. The invariants are ordered around the extracted edge chain which helps match the invariants from di erent scenes. The deviations show that the conic/line-pair invariants are less stable than the ve line invariants. The rst invariants shown, which have di erences of 9.8% to the mean, would not be accepted as the error is too large.

1991

Cited by 8

### Table A.1. Definitions Revisited

1998

Cited by 21

### Table 3: Behaviour reduction revisited

2004

"... In PAGE 29: ... In Tables 1 and 3, we de ne the reduction of behaviours as a big step semantics. We reformulate the rules in Table3 , following a small step approach: (b02) (f+(~ p); yield:b; ; s) ! (f+(~ p); b; ; s) (b05) (f+(~ p); match r with j p ) b j : : : ; ; s) ! (f +([p=r]~ p); b; 1 ; s) if 1p = s(r) (b06) (f+(~ p); g(~e); ; s) ! (g+(~ q;~rg); b; 1; s) if ~e + ~v; g(~ q) = b; 1~ q = ~v (b07) (f+(~ p); r := e:b; ; s) ! (f+(~ p); b; ; s[v=r]) if e + v Note that there are no rules corresponding to (b1), (b3), (b4) since these rules either terminate or suspend the computation of the thread in the instant. If (f +(~ p); b; ; s) ! (g+(~ q); b0; 0; s0) by the rules above then some register variables r1; : : : ; rk in ~ p can be instan- tiated.... ..."

Cited by 13

### Table 9: The results of the computed invariants from Euclidean reconstruction of the conics. The computed invariants are very accurate and stable in both cases.

1995

Cited by 4

### Table 3: Learning contingencies across gaps (revisited)

1992

"... In PAGE 6: ... A reduced description network had no problem learning the contingency across wide gaps. Table3 compares the results presented earlier for a standard net with ten context units and the results for an RD net having six standard context units ( = 0) and four units having identical nonzero , in the range of .... ..."

Cited by 62

### Table 5: Encoding of -calculus operators and types, revisited

"... In PAGE 11: ... This is in general quite restrictive, because ambients form a hierarchical structure, in which I/O requests may be arbitrarily deep. Table5 shows an encoding that does not su er these problems; the solution adopted in [7] called the coalescing encoding is behaviourally quite di erent. Both input and output requests along channel p are represented by ambients called p.... In PAGE 11: ...[in p.open p. (q1. . qn). open p. [[P]] ] j open p This complicates however the correctness proof (we would need for instance type systems for linear types). In the encoding of Table5 , coactions guarantee that ex- actly one output ambient enters an input one and, in the simpler input clause above, they control when the input am- bient is opened. 7 Related work and conclusions The introduction of coactions has been inspired by the SOS... ..."

### Table 1: Summary of applications of various configurations of conics

2004

"... In PAGE 6: ... The algorithms are simple and do not require correspondences of a large number of conics or the solution of multivariate polynomial equations. A summary of the results is shown in Table1 . Extensions to these methods which handle over determined sets of equations, obtained due to the presence of more than two coplanar conics, need to be explored.... ..."

Cited by 4

### Table 3 shows the computed for each pair of conics. The absolute value of increases with the increasing discrepancy of conic pairs. Note that as C1 is a slightly deformed version of C0 and C0 1 of C0 0, it is quite reasonable that C1 is as close to C0 0 as to C0 1, as suggested in the table.

1995

"... In PAGE 16: ... Table3 : The computed for each pair of conics Ci and C0 j, Ci is a conic of the rst image and C0 j a conic of the second image. 5.... ..."

Cited by 4

### Table 2: Duality for closed conic convex programs

"... In PAGE 23: ...d #03 = inf 8 #3E #3C #3E : s 5 #0C #0C #0C #0C #0C #0C #0C 2 6 4 0 1 0 1 s 2 s 5 = p 2 0 s 5 = p 2 0 3 7 5 #17 0 9 #3E = #3E ; = 1: Finally, the possibility of the entries in Table2 where weak infeasibility is not involved, can be demonstrated by a 2-dimensional linear programming problem: Example 5 Let n =2,c2#3C 2 ,K=K #03 =#3C 2 + and A = f#28x 1 ;x 2 #29jx 1 =0g; A ? =f#28s 1 ;s 2 #29js 2 =0g: We see that #28P#29 is strongly feasible if c 1 #3E 0,weakly feasible if c 1 =0and strongly infeasible if c 1 #3C 0. Similarly, #28D#29 is strongly feasible if c 2 #3E 0,weakly feasible if c 2 =0and strongly infeasible if c 2 #3C 0.... In PAGE 27: ... #0F The regularizedprogram CP#28b; c; A; K 0 #29 is dual strongly infeasible if and only if F D = ;. Combining Theorem 8 with Table2 , we see that the regularized conic convex program is in perfect duality: Corollary 7 Assume the same setting as in Theorem 8. Then there holds #0F If d #03 = 1, then the regularized primal CP#28b; c; A; K 0 #29 is either infeasible or unbounded.... ..."