Fundamentals of digital image processing
Fundamentals of digital image processing
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
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
Introduction: a Bayesian formulation of visual perception
Perception as Bayesian inference
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Content-based video sequence representation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Information-theoretic image formation
IEEE Transactions on Information Theory
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
Occlusion-Based accurate silhouettes from video streams
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Virtual object placement in video for augmented reality
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
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Motion segmentation methods often fail to detect the motions of low textured regions. We develop an algorithm for segmentation of low textured moving objects. While usually current motion segmentation methods use only two or three consecutive images our method refines the shape of the moving object by processing successively the new frames as they become available. We formulate the segmentation as a parameter estimation problem. The images in the sequence are modeled taking into account the rigidity of the moving object and the occlusion of the background by the moving object. The segmentation algorithm is derived as a computationally simple approximation to the Maximum Likelihood estimate of the parameters involved in the image sequence model: the motions, the template of the moving object, its intensity levels, and the intensity levels of the background pixels. We describe experiments that demonstrate the good performance of our algorithm.