Abstract A novel video object segmentation method is proposed which aims at combining color and motion information. The model has a multi-layer structure: Each feature has its own layer, called feature layer, where a classical Markov random field (MRF) image segmentation model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model, called combined layer, which interacts with each feature layer and provides the segmentation based on the combination of different features. Unlike previous methods, our approach doesn’t assume motion boundaries being part of spatial ones. Therefore a very important property of the proposed method is the ability to detect boundaries that are visible only in the motion feature as well as those visible only in the color one. The method is validated on synthetic and real video sequences.
A Multi-Layer MRF Model for Video Object Segmentation
Computer Vision – ACCV 2006 ; 8 ; 953-962
Lecture Notes in Computer Science ; 3852 , 8
2006-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
Markov Random Field , Feature Layer , Color Layer , Motion Segmentation , Markov Random Field Model Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
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