### Table 12: Finite Element Discretizations implemented with UG.

1997

Cited by 66

### TABLE 3.2 Finite Element Discretization

### Table 4. Average mean and standard deviation of texture and shape description elements.

"... In PAGE 14: ...ults are excellent. The average standard deviation is 0.3. Edge Histogram performs excellently on any type of media (see Table4 for details on tex- ture and shape descriptors). The Homogeneous Texture descriptor per- forms poorly on colour images, especially if they have few colour shades and textures in them.... ..."

### Table 4. Average mean and standard deviation of texture and shape description elements.

2004

"... In PAGE 11: ...elow 0.15. This means that Scalable Color is hardly discriminant for synthetic content. Edge Histogram performs excellently on any type of media (see Table4 for details on texture and shape descriptors). Even on coats-of-arms images with hardly any textures present (but, of course, very sharp edges) the average standard deviation is above 0.... ..."

Cited by 3

### Table 2: Free and xed elements in the discrete time parameter matrices of the EDM

"... In PAGE 16: ... Model speci cation While different models were speci ed, all had the same measurement equation part, which will be addressed rst: yti = Ctixti + dti + vti with cov(vti) = Rti : (20) The parameter matrices for successive observation time points t0; t1; t2; t3 are shown in Table 2. By xing the factor loading of the One-Minute-Test Form A at value 1 and its measurement origin at value 0 (in the rst row of Ct0 and dt0 in Table2 ) we equalled the variance and mean of the latent Decoding Skill (DS) variable at the initial time point t0 to the true variance (total variance minus measurement error variance) and mean of the One-Minute-Test Form A at that time point. In the same manner, the true variance and mean of Cito Reading Comprehension Test 2 de ned the variance and mean of the latent Reading Comprehension (RC) variable at t0.... In PAGE 18: ....H.L. Oud The parameter matrices of the discrete time state equation xt = A xt t + + b + wt t with cov(wt t) = Q (21) are also shown in Table2 . As they contain 21 unknown parameters, the total number of pa- rameters to be estimated is 38.... In PAGE 18: ... The stochastic differential equation dx(t) dt = Ax(t) + + b + GdW(t) dt ; (22) describes the development of the latent variables in continuous time, containing in particular con- tinuously contributing traits and constants b. The EDM relates the continuous time parameter matrices in Equation (22) as follows to the discrete time parameter matrices in Table2 (Oud amp; Jansen, 2000): A = eA t ; b = A 1[A I] b ; Q = irow[(A I + I A) 1(A A I I) row(GG0)] ; = A 1[A I] [A0 I]A0 1 ; ;xt0 = A 1[A I] ;xt0 : (23) Here, is the Kronecker product, row is the rowvec operation, putting the elements of a matrix rowwise in a column vector, irow the inverse operation. Because the time intervals between the measurements were approximately half a year, we started by xing t for the intervals t1 t0; t2 t1; t3 t2 at = 0:50.... ..."

### Table 5.1: Discrete encodings of the element concentrations.

2007

### Table 1: Example from the Textures DB

"... In PAGE 4: ...s the texture itself, i.e. the energy value returned by apply- ing a tree-structured wavelet transform (TSWT) to the tex- ture (see (Chang amp; Kuo 1993)). For each texture, we keep a confidence vector with one element for each indoor object (see Table1 ), denoted in the set of parameters Apar. These values interpret an object belonging probability distribution, i.... ..."

### Table 6. Texture/Anisotropy Rankings

"... In PAGE 7: ...able 5. Texture Characteristics @ t/2 vs. Yield Strength Anisotropy....................................... 12 Table6... In PAGE 19: ... In contrast, when the IPA coefficient or Kurtosis analysis is employed, the 2195 Base region appears to be the most anisotropic and the 2096 Skin region the most isotropic. The rankings derived by using the various approaches are shown in Table6 , categorized into Skin and Base regions for correlation purposes. Based on the assumption that increasing amounts of RX-related texture elements should result in more isotropic yield strength behavior [19], there should be a correlation between the rankings listed in Table 6.... ..."