8,142 research outputs found
Smooth foreground-background segmentation for video processing
We propose an efficient way to account for spatial smoothness in foreground-background segmentation of video sequences. Most statistical background modeling techniques regard the pixels in an image as independent. and disregard the fundamental concept of smoothness. In contrast, we model smoothness of the foreground and background with a Markov random field, in such a way that it can be globally optimized at video frame rate. As a background model, the mixture-of-Gaussian (MOG) model is adopted and enhanced with several improvements developed for other background models. Experimental results show that the MOG model is still competitive, and that segmentation with the smoothness prior outperforms other methods
Screen Content Image Segmentation Using Sparse-Smooth Decomposition
Sparse decomposition has been extensively used for different applications
including signal compression and denoising and document analysis. In this
paper, sparse decomposition is used for image segmentation. The proposed
algorithm separates the background and foreground using a sparse-smooth
decomposition technique such that the smooth and sparse components correspond
to the background and foreground respectively. This algorithm is tested on
several test images from HEVC test sequences and is shown to have superior
performance over other methods, such as the hierarchical k-means clustering in
DjVu. This segmentation algorithm can also be used for text extraction, video
compression and medical image segmentation.Comment: Asilomar Conference on Signals, Systems and Computers, IEEE, 2015,
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