Waviz: Spectral Similarity for Object Detection

Previous attemps to perform figure-ground segmentation have universally made the assumption that observations of the scene are independent in time. In the vocabulary of the stochastic systems literature: the individual pixels are taken to be samples from a stationary, white random processes with independent increments. Many scenes that could loosley be referred to as static often contain cyclostationary processes: meaning that there is significant structure in the correlations between observations across time. A tree swaying in the wind or a wave lapping on a beach is not just a collection of randomly shuffled appearances, but a physical system that has characteristic frequency responses associated with its dynamics. Our novel method leverages this fact to perform object detection based solely on the dynamics, rather than the appearance, of the pixels in a scene. Results are presented for a challenging scene containing wave activity in the background that visually masks a low-contrast foreground target.