In [8], the authors proposed a wavelet-based MAR algorithm in which the missing projections are onedimensionally interpolated (along projection profiles) by a weighted sum of the wavelet coefficients of the corrupted projections and those of linearly interpolated projections.
Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result
Sample text:```13 indicates the comparison of the results of the mesh simplification method [2] after applying different mesh enhancement techniques.```
Sample intent: background



Sample text:```Following past work, we show that such basis models can be learned from 2D annotations using techniques adapted from nonrigid structure from motion (SFM) [20, 19].```
Sample intent: method



Sample text:```rural, we calculated the relative prevalence of a missing value by other demographic and fitness variables using logistic regression [49].```
Sample intent: background



Sample text:```Over the last few years, many different local interest point detectors (like Harris3D [16], Cuboid [6], Hessian [35] or Dense sampling [33]) and many spatio-temporal descriptors (like HOG [17], HOG3D [14], HOF [17], Cuboid [6] or ESURF [35]) have been proposed.```
Sample intent: method



Sample text:```Our results confi rm previous studies that reached the same conclusion (Boushey et al 1994; Vermeulen et al 2000; Till et al 2005; Marcucci et al 2005).```
Sample intent: result
Sample text:```As a novel biomechanical analysis from video recordings, a Model-Based Image-Matching (MBIM) motion analysis technique has been introduced to investigate human motion from uncalibrated video sequences (Krosshaug and Bahr, 2005).```
Sample intent: background



Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result



Sample text:```The results for veridical recall replicated previous results in the literature (Bal-
lardini et al., 2008; McDermott & Watson, 2001), with the average recall of studied words increas-
ing monotonically as SOA increased.```
Sample intent: result



Sample text:```This seems to be the main drawback of the current implementation in comparison to the method of Black Jepson (“refined” in Table 1).```
Sample intent: result



Sample text:```In-between methods can be subdivided into linear approximations [12,24] and quadratic interpolation methods [22,23,25–27] based on the degree of the polynomial approximation employed.```
Sample intent: method



Sample text:```rural, we calculated the relative prevalence of a missing value by other demographic and fitness variables using logistic regression [49].```
Sample intent: background



Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
Sample intent: result



Sample text:```Over the last few years, many different local interest point detectors (like Harris3D [16], Cuboid [6], Hessian [35] or Dense sampling [33]) and many spatio-temporal descriptors (like HOG [17], HOG3D [14], HOF [17], Cuboid [6] or ESURF [35]) have been proposed.```
Sample intent: method



Sample text:```13 indicates the comparison of the results of the mesh simplification method [2] after applying different mesh enhancement techniques.```
Sample intent: background



Sample text:```The adopted theoretical formulation expands on the USVA theory for two confocal beams in the case of lossless media [4,5], by also including absorption in the analytical model.```
Sample intent: method