Your English writing platform
Discover LudwigExact(17)
where and represent the length of the feature trajectories for sounds and, respectively.
Fig. 4 Temporal filtering of feature trajectories versus linear transformation of feature vectors.
We compare the motion smoothing results visually by showing the feature trajectories in the stabilized videos.
We propose a novel representation of the first person actions derived from feature trajectories.
This is a method frequently used in speech recognition where feature trajectories can contain extra information in the feature vector.
(2) The feature trajectories are then grouped based on consistent common motion in order to define a single moving object. .
Similar(43)
These HMM templates encode whether the feature trajectory varies in a constant (high or low), increasing/decreasing, or more complex (up → down; down → up) fashion.
To compare environmental sounds in a likelihood-based manner, a hidden Markov model (HMM) is estimated from the th feature trajectory of sound.
Specifically, to predict the feature at frame t and dimension d, (y_{t}^{(d)}), we only use the local feature trajectory and feature vector that contains (x_{t}^{(d)}) as shown in Fig. 5.
All features are modeled as conditionally independent given the corresponding HMM, that is, the likelihood that the feature trajectory of sound was generated by the HMM built to approximate the simple feature trends of sound is (10).
Several motion smoothing methods are available for motion intention estimation such as particle filter[10], Kalman filter[11], Gaussian filter[25, 26], adaptive filter[26, 27], spline smoothing[28, 29], or point feature trajectory smoothing[30, 31].
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com