Saliency detection for videos has been paid great attention and extensively studied in recent years. However, various visual scene with complicated motions leads to noticeable background noise and non-uniformly highlighting the foreground objects. In this paper, we proposed a video saliency detection model using spatio-temporal cues. In spatial domain, the location of foreground region is utilized as spatial cue to constrain the accumulation of contrast for background regions. In temporal domain, the spatial distribution of motion-similar regions is adopted as temporal cue to further suppress the background noise. Moreover, a backward matching based temporal prediction method is developed to adjust the temporal saliency according to its corresponding prediction from the previous frame, thus enforcing the consistency along time axis. The performance evaluation on several popular benchmark data sets validates that our approach outperforms existing state-of-the-arts.

In this paper, we propose the Perceptual Shape Decomposition (PSD) to detect fingers for a Kinect-based hand gesture recognition system. The PSD is formulated as a discrete optimization problem by removing all negative minima with minimum cost. Experiments show that our PSD is perceptually relevant and robust against distortion and hand variations, and thus improves the recognition system performance.