Motion segmentation and qualitative dynamic scene analysis from an image sequence
International Journal of Computer Vision
Computing occluding and transparent motions
International Journal of Computer Vision
Variational methods in image segmentation
Variational methods in image segmentation
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour extraction of moving objects in complex outdoor scenes
International Journal of Computer Vision
Compact Representations of Videos Through Dominant and Multiple Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Geometric partial differential equations and image analysis
Geometric partial differential equations and image analysis
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Maximum Likelihood Estimation of the Template of a Rigid Moving Object
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Content-based video sequence representation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
IEEE Transactions on Image Processing
Figure-ground segmentation from occlusion
IEEE Transactions on Image Processing
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We address the problem of segmenting out moving objects from video. The majority of current approaches use only the image motion between two consecutive frames and fail to capture regions with low spatial gradient, i.e., low textured regions. To overcome this limitation, we model explicitly: i) the occlusion of the background by the moving object and ii) the rigidity of the moving object across a set of frames. The segmentation of the moving object is accomplished by computing the Maximum Likelihood (ML) estimate of its silhouette from the set of video frames. To minimize the ML cost function, we developed a greedy algorithm that updates the object silhouette, converging in few iterations. Our experiments with synthetic and real videos illustrate the accuracy of our segmentation algorithm.