1 research outputs found
Fully automatic extraction of salient objects from videos in near real-time
Automatic video segmentation plays an important role in a wide range of
computer vision and image processing applications. Recently, various methods
have been proposed for this purpose. The problem is that most of these methods
are far from real-time processing even for low-resolution videos due to the
complex procedures. To this end, we propose a new and quite fast method for
automatic video segmentation with the help of 1) efficient optimization of
Markov random fields with polynomial time of number of pixels by introducing
graph cuts, 2) automatic, computationally efficient but stable derivation of
segmentation priors using visual saliency and sequential update mechanism, and
3) an implementation strategy in the principle of stream processing with
graphics processor units (GPUs). Test results indicates that our method
extracts appropriate regions from videos as precisely as and much faster than
previous semi-automatic methods even though any supervisions have not been
incorporated.Comment: submitted to Special Issue on High Performance Computation on
Hardware Accelerators, the Computer Journa