186 frames per second (on 640×480 pixels CalTech images), which is ranked eighth compared with 15 detectors evaluated in (Dollár et al. 2012).
Sample text:```Apical color M-mode recordings were obtained at a depth of 16 to 18 cm and a Nyquist limit of 18 to 25 cm/s (15).```
Sample intent: method
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:```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:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
Sample intent: method



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:```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:```These are in keeping with earlier studies on the steadystate response (2, 5, 8), step-function response, (3, 5, 6, 14) and sinusoidal response ( 14) of the various vascular beds.```
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:```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:```The present observations support and extend earlier contentions that Parkinson patients prefer to adopt a closedloop control mode, adjusting their movements continuously on the basis of available visual information (Flowers 1976).```
Sample intent: result



Sample text:```Similarly, in a study on 60 subjects, blinks were measured at the beginning and end of a working day (Caffier et al., 2003). Although no significant difference between alert and drowsy phases was found, a slightly decreased blink rate during drowsy phase was reported. However, these results contradict the findings of Morris and Miller (1996) about the efficiency of F in a moving base-flight simulator with 10 sleep-deprived pilots.```
Sample intent: result



Sample text:```5 concentrations were computed for each participant using a hierarchical spatiotemporal model fit with a pragmatic estimation procedure described elsewhere [30,31].```
Sample intent: background



Sample text:```05, minimum cluster size 64 voxels) [Woo et al., 2014] for the primary analysis of regions showing increasing activity with distance from the home location.```
Sample intent: method



Sample text:```We used longitudinal sequence samples for 15 patients from two independent studies [13,22].```
Sample intent: background



Sample text:```The electrode locations, acquired with a post-implantation CT were projected on the T1weighted individual anatomy scan (Hermes et al. 2010).```
Sample intent: background



Sample text:```Apical color M-mode recordings were obtained at a depth of 16 to 18 cm and a Nyquist limit of 18 to 25 cm/s (15).```
Sample intent: method