Optical flow is a classical approach for this problem; however, it depends on robust gradient estimation and will fail for noisy, underexposed, or overexposed images.
GLTP uses more robust gradient magnitude values as opposed to grey levels with a three-level encoding scheme to discriminate between smooth and highly textured facial regions.
Since typical optical flow techniques depend on robust gradient estimates and would fail on noisy low-contrast frames, we pre-enhance each frame by SCT filter and the adaptive piecewise mapping function (APMF).
This paper addresses the efficient implementation of a robust gradient-based optical flow model in a low-power platform based on a multi-core digital signal processor (DSP).
A gradient family-based method is presented in the contribution 'Robust motion estimation on a low-power multi-core DSP' [17] authored by Francisco D. Igual et al., which addresses the efficient implementation of a robust gradient-based optical flow model in a low-power platform based on a multi-core digital signal processor (DSP C6678 DSP from Texas Instruments, Dallas, TX, USA).
Recent successes in modeling include the prediction of phenotypes [19], the functioning of the Epidermal-Growth-Factor receptors [20], the determination of the left-right axis in vertebrates [21], [22] and the formation of robust gradients [23], [24].
A robust nutrient gradient, driven mostly by dissolved inorganic nitrogen, from alongshore to offshore in the main Bay, was a large determinant in the spatial clustering.
The fully-recurrent Robust Adaptive Gradient Descent (RAGD) algorithm, is tuned initially using synthesised crank kinematics, and then tested on real engine data to assess the reconstruction capability.
Notably, only chordin, but not bmp4 expression needs to be spatially restricted for robust signaling gradient formation.
Similarly, ONT1, a secreted Olfactomedin-class protein, facilitates Chordin-BMP1 interaction by independently recruiting both proteins, resulting in robust BMP gradient formation during embryonic DV patterning.
The larva achieves robust odor gradient ascents through an alternation of approximately straight runs and turning events (Gomez-Marin et al., 2011; Gershow et al., 2012).
