We investigate the estimation of illuminance flow using Histograms of Oriented Gradient features (HOGs). In a regression setting, we found for both ridge regression and support vector machines, that the optimal solution shows close resemblance to the gradient based structure tensor (also known as the second moment matrix). Theoretical results are presented showing in detail how the structure tensor and the HOGs are connected. This relation will benefit computer vision tasks such as affine invariant texture/object matching using HOGs. Several properties of HOGs are presented, among others, how many bins are required for a directionality measure, and how to estimate HOGs through spatial averaging that requires no binning.

In most real-time applications, deadlines are artifices that need to be enforced to meet different performance requirements. For example, in periodic task sets, jitter requirements can be met by assigning suitable relative deadlines and guaranteeing the feasibility of the schedule. This paper presents a method (called minD) for calculating the minimum EDF-feasible deadline of a real-time task. More precisely, given a set of periodic tasks with hard real-time requirements, which is feasible under EDF, the proposed algorithm allows computing the shortest deadline that can be assigned to an arbitrary task in the set, or to a new incoming task (periodic or aperiodic), still preserving the EDF feasibility of the new task set. The algorithm has a pseudo polynomial complexity and handles arbitrary relative deadlines, which can be less than, equal to, or greater than periods