Dynamic Spatial Predicted Background.

We present a novel method for online background modeling for static video cameras - Dynamic Spatial Predicted Background (DSPB). Our unique method employs a small subset of image pixels to predict the whole scene by exploiting pixel correlations (distant and close). DSPB acts as a hybrid model combining successful elements taken from two major approaches: local-adaptive that propose to fit a distribution pixelwise, and global-linear that reconstruct the background by finding a lowrank version of the scene.

Diurnal and seasonal variation of cough episodes in healthy young adults.

Setting: We developed an algorithm to assess recorded cough episodes and differentiate them from similar, non-cough sounds.

Objective: To measure cough episodes in healthy young adults, cigarette smokers and non-smokers over a 24-hour recording period, during the course of normal activity.

Design: The study subjects were students, aged 20-40 years old.