Automated Optical Reading of Scanned ECGs

Electrocardiogram (ECG) is a valuable tool for medical diagnosis used worldwide. Its use has contributed significantly to the prevention of cardiovascular diseases including infarctions. Although physicians need to see the printed curves for a diagnosis, nowadays there exist automated tools based on machine learning that can help diagnosis of arrhythmias and other pathologies, these tools operate on digitalized ECG data that are merely one-dimensional discrete signals (a kind of information that is much similar to digitized audio). Thus, it is interesting to have both the graphical information and the digitized data. This is possible with modern, digital equipment. Nevertheless, there still exist many analog electrocardiogram machines that plot results on paper with a printed gris measured in millimeters. This paper presents a novel image analysis method that is capable of reading a printed ECG and converting it into a sampled digital signal.

PRNU Based Source Camera Identification for Webcam and Smartphone Videos

This communication is about an application of image forensics where we use camera sensor fingerprints to identify source camera (SCI: Source Camera Identification) in webcam/smartphone videos. Sensor or camera fingerprints are based on computing the intrinsic noise that is always present in this kind of sensors due to manufacturing imperfections. This is an unavoidable characteristic that links each sensor with its noise pattern. PRNU (Photo Response Non-Uniformity) has become the default technique to compute a camera fingerprint. There are many applications nowadays dealing with PRNU patterns for camera identification using still images. In this work we focus on video, first on webcam video and afterwards on smartphone video. Webcams and smartphones are the most used video cameras nowadays. Three possible methods for SCI are implemented and assessed in this work.