Multiple classifier-based spatiotemporal features for living activity prediction

Automated Detection of Multiple Sclerosis Lesions Using Texture-based Features and a Hybrid Classifier

Background: Multiple Sclerosis (MS) is the most frequent non-traumatic neurological disease capable of causing disability in young adults. Detection of MS lesions with magnetic resonance imaging (MRI) is the most common technique. However, manual interpretation of vast amounts of data is often tedious and error-prone. Furthermore, changes in lesions are often subtle and extremely unrepresentati...

Spatiotemporal features for asynchronous event-based data

Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm shift in visual information processing. These new sensors rely on a stimulus-driven principle of light acquisition similar to biological retinas. They are event-driven and fully asynchronous, thereby reducing redundancy and encoding exact times of input signal changes, leading to a very precise temporal re...

Classifier-Based Capacity Prediction for Desktop Grids

Availability of resources in desktop grids is characterized by high dynamicity, a consequence of the local (owner) control policies in such systems. The efficient usage of desktop computers can therefore greatly benefit from prediction methodologies, as those help to estimate the short-term behavior of resources and to take the appropriate preventive actions. We present a prediction study perfo...

Dynamic Facial Expression Recognition Using Boosted Component-Based Spatiotemporal Features and Multi-classifier Fusion

Feature extraction and representation are critical in facial expression recognition. The facial features can be extracted from either static images or dynamic image sequences. However, static images may not provide as much discriminative information as dynamic image sequences. On the other hand, from the feature extraction point of view, geometric features are often sensitive to the shape and r...

Evaluating spatiotemporal interest point features for depth-based action recognition