Quaternion-Based Gesture Recognition Using Wireless Wearable Motion Capture Sensors.

This work presents the development and implementation of a unified multi-sensor human motion capture and gesture recognition system that can distinguish between and classify six different gestures. Data was collected from eleven participants using a subset of five wireless motion sensors (inertial measurement units) attached to their arms and upper body from a complete motion capture system. We compare Support Vector Machines and Artificial Neural Networks on the same dataset under two different scenarios and evaluate the results.

Image registration under illumination variations using region-based confidence weighted M-estimators.

We present an image registration model for image sets with arbitrarily shaped local illumination variations between images. Any nongeometric variations tend to degrade the geometric registration precision and impact subsequent processing. Traditional image registration approaches do not typically account for changes and movement of light sources, which result in interimage illumination differences with arbitrary shape.