To optimize the homogenized properties of the unit cell we again use topology optimization and to make this unit cell optimization problem well posed we use restriction and thereby obtain a minimum microstructural length scale.
Since we deal with estimating driver head pose, we have annotated our sequence using three classes for pitch and three classes for yaw. Figure 5 represents an example of frames corresponding to each class according to the pitch and yaw angles.
Since these types of problems are ill posed, we have used a regularized solution.
Thank you for coining the phrase, Happy Boyfriend Pose... We use it over the hill here, too.
In order to achieve head-pose estimation we use a feature vector based on skin detection to estimate the orientation of the head, which is discretised into 8 different orientations, relative to the camera.
Nevertheless, at the beginning, the question posed was should we use and release the GDMOs or not?
It also poses questions about how we use photography (these are, generally, photographs of photographs).
We pose the widely used bag-of-visual-words (BoVW) [5] method as a neural network, which allows it to refine the feature extraction and clustering functions by backpropagating the classification error.
We use our pose estimation and alignment technique and we generate 3 views per 3D object.
No pose data is available and we use the bounding box data from [21] as ground truth.
To address the challenges that outliers pose for some statistical models, we use the Huber–White sandwich estimators (Huber 1967; White 1980) in all our models, thereby obviating minor concerns about the potential failure to meet assumptions, such as normality, heteroskedasticity, or observations that exhibit large residuals, leverage, or influence.
