Online learning neural tracker

Online learning neural tracker

Object tracking is a fundamental computer vision problem and is required for many high-level tasks such as activity recognition, behavior analysis and surveillance. The main challenge in the object tracking problem is the dynamic change in object/background appearance, illumination, shape and occlusion. We present an online learning neural tracker (OLNT) to differentiate the object from the bac...

A Higher Order Online Lyapunov-Based Emotional Learning for Rough-Neural Identifiers

o enhance the performances of rough-neural networks (R-NNs) in the system identification‎, ‎on the base of emotional learning‎, ‎a new stable learning algorithm is developed for them‎. ‎This algorithm facilitates the error convergence by increasing the memory depth of R-NNs‎. ‎To this end‎, ‎an emotional signal as a linear combination of identification error and its differences is used to achie...

The Cdf-ii Online Silicon Vertex Tracker

The Online Silicon Vertex Tracker is the new CDF-II level 2 trigger processor designed to reconstruct 2-D tracks within the Silicon Vertex Detector with high speed and accuracy. By performing a precise measurement of impact parameters the SVT allows tagging online B events which typically show displaced secondary vertices. Physics simulations show that this will greatly enhance the CDF-II B-phy...

Backlash compensation by neural-network online learning

Online neural monitoring of statistical learning.

The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for ...