Digital video processing
Machine vision
Digital Image Processing
Applications of Video-Content Analysis and Retrieval
IEEE MultiMedia
Consistent Segmentation for Optical Flow Estimation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Motion segmentation using Markov random field model for accurate moving object segmentation
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Moving object segmentation by background subtraction and temporal analysis
Image and Vision Computing
Automatic segmentation of moving objects for video object plane generation
IEEE Transactions on Circuits and Systems for Video Technology
An efficient two-pass MAP-MRF algorithm for motion estimation based on mean field theory
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
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This paper provides an automatic segmentation method of non-rigid objects in image sequences. The non-rigid objects have fuzzy, blurred, and indefinite boundaries such as smoke and clouds, and are random and unpredictable in spatial and temporal domains. To segment the non-rigid objects, a new segmentation approach considering random and unpredictable characteristics of the non-rigid objects is needed. In this paper, we propose a new segmentation method of the non-rigid objects in image sequences using spatiotemporal information. The procedure toward complete segmentation consists of three steps: spatial segmentation, temporal segmentation, and fusion of the spatial and temporal segmentation results. By means of experiments on various test sequences, we demonstrate that the performance of our method is quite impressive from the viewpoints of the segmentation accuracy.