Feature encoding with respect to an over-complete dictionary learned by unsupervised methods, followed by spatial pyramid pooling, and linear classification, has exhibited powerful strength in various vision applications. Here we propose to use the feature learning pipeline for visual tracking. Tracking is implemented using tracking-bydetection and the resulted framework is very simple yet effe...

One of the most promising methods of interacting with small portable computing devices such as personal digital assistants is the use of handwriting. However, for data acquisition touch sensitive pads, which are limited in size, and special pens are required. In order to render this communication method more natural Munich & Perona 11] proposed to visually observe the writing process on ordinar...

We study the tracking problem, namely, estimating the hidden state of an object over time, from unreliable and noisy measurements. The standard framework for the tracking problem is the generative framework, which is the basis of solutions such as the Bayesian algorithm and its approximation, the particle filters. However, these solutions can be very sensitive to model mismatches. In this paper...

The dynamics behind human migrations are very complex. Economists have intensely studied them because of their importance for the global economy. However, tracking migration is costly, and available data tends to be outdated. Online data can be used to extract proxies for migration flows, and these proxies would not be meant to replicate traditional measurements but are meant to complement them...

User preferences of music genres can significantly changes over time depending on fashions and the personal situation of music consumers. We propose a model to learn user preferences and their changes in an adaptive way. Our approach refines a model for user preferences by explicitly considering two plausible constraints of computational costs and limited storage space. The model is required to...

This paper presents GRASTA (Grassmannian Robust Adaptive Subspace Tracking Algorithm), an efficient and robust online algorithm for tracking subspaces from highly incomplete information. The algorithm uses a robust l-norm cost function in order to estimate and track non-stationary subspaces when the streaming data vectors are corrupted with outliers. We apply GRASTA to the problems of robust ma...

This paper describes a synergistic integration of a grasping simulator and a real-time visual tracking system, that work in concert to 1) find an object’s pose, 2) plan grasps and movement trajectories, and 3) visually monitor task execution. Starting with a CAD model of an object to be grasped, the system can find the object’s pose through vision which then synchronizes the state of the robot ...

One of the major challenges of model-free visual tracking problem has been the difficulty originating from the unpredictable and drastic changes in the appearance of objects we target to track. Existing methods tackle this problem by updating the appearance model on-line in order to adapt to the changes in the appearance. Despite the success of these methods however, inaccurate and erroneous up...

Process Mining techniques have been gaining attention, owing to their potentiality to extract compact process models from massive logs. Traditionally focused on workflows, these techniques tend to rely on a clear specification of process tasks, assumed to be referred explicitly by the logs. This limits however their applicability to many real-life BPM environments (e.g. issue tracking systems) ...