Contribution of Different Handwriting Modalities to Differential Diagnosis of Parkinson's Disease

In this paper, we evaluate the contribution of different handwriting modalities to the diagnosis of Parkinson's disease. We analyse on-surface movement, in-air movement and pressure exerted on the tablet surface. Especially in-air movement and pressure-based features have been rarely taken into account in previous studies. We show that pressure and in-air movement also possess information that is relevant for the diagnosis of Parkinson's Disease (PD) from handwriting. In addition to the conventional kinematic and spatio-temporal features, we present a group of the novel features based on entropy and empirical mode decomposition of the handwriting signal. The presented results indicate that handwriting can be used as biomarker for PD providing classification performance around 89% area under the ROC curve (AUC) for PD classification.

On the focusing of thermal images

In this paper we present a new thermographic image database suitable for the analysis of automatic focus measures. This database consists of 8 different sets of scenes, where each scene contains one image for 96 different focus positions. Using this database we evaluate the usefulness of six focus measures with the goal to determine the optimal focus position. Experimental results reveal that an accurate automatic detection of optimal focus position is possible, even with a low computational burden. We also present an acquisition tool able to help the acquisition of thermal images. To the best of our knowledge, this is the first study about automatic focus of thermal images.

A new face database simultaneously acquired in visible, near infrared and thermal spectrum

In this paper we present a new database acquired with three different sensors (visible, near infrared and thermal) under different illumination conditions. This database consists of 41 people acquired in four different acquisition sessions, five images per session and three different illumination conditions. The total amount of pictures is 7.380 pictures. Experimental results are obtained through single sensor experiments as well as the combination of two and three sensors under different illumination conditions (natural, infrared and artificial illumination). We have found that the three spectral bands studied contribute in a nearly equal proportion to a combined system. Experimental results show a significant improvement combining the three spectrums, even when using a simple classifier and feature extractor. In six of the nine scenarios studied we obtained identification rates higher or equal to 98%, when using a trained combination rule, and two cases of nine when using a fixed rule.