MAP-Based Stochastic Diffusion for Stereo Matching and Line Fields Estimation
International Journal of Computer Vision
Adaptable Neural Networks for Unsupervised Video Object Segmentation of Stereoscopic Sequences
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Neural Networks Retraining for Unsupervised Video Object Segmentation of Videoconference Sequences
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A fast automatic VOP generation using boundary block segmentation
Real-Time Imaging
Unsupervised video object segmentation and tracking based on new edge features
Pattern Recognition Letters
Hybrid Morphology Processing Unit Architecture for Moving Object Segmentation Systems
Journal of VLSI Signal Processing Systems
Object based segmentation of video using variational level sets
Machine Graphics & Vision International Journal
Compression of Patient Monitoring Video Using Motion Segmentation Technique
Journal of Medical Systems
An application of MAP-MRF to change detection in image sequence based on mean field theory
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
Multiple moving object detection for fast video content description in compressed domain
EURASIP Journal on Advances in Signal Processing
Objects based change detection in a pair of gray-level images
Pattern Recognition
System Level Design and Implementation for Region-of-Interest Segmentation
Journal of Signal Processing Systems
Reconfigurable Morphological Image Processing Accelerator for Video Object Segmentation
Journal of Signal Processing Systems
Stereo-based object segmentation combining spatio-temporal information
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Fire detection algorithms for video images of large space structures
Multimedia Tools and Applications
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To provide multimedia applications with new functionalities, the new video coding standard MPEG-4 relies on a content-based representation. This requires a prior decomposition of sequences into semantically meaningful, physical objects. We formulate this problem as one of separating foreground objects from the background based on motion information. For the object of interest, a 2D binary model is derived and tracked throughout the sequence. The model points consist of edge pixels detected by the Canny operator. To accommodate rotation and changes in shape of the tracked object, the model is updated every frame. These binary models then guide the actual video object plane (VOP) extraction. Thanks to our new boundary postprocessor and the excellent edge localization properties of the Canny operator, the resulting VOP contours are very accurate. Both the model initialization and update stages exploit motion information. The main assumption underlying our approach is the existence of a dominant global motion that can be assigned to the background. Areas that do not follow this background motion indicate the presence of independently moving physical objects. Two alternative methods to identify such objects are presented. The first one employs a morphological motion filter with a new filter criterion, which measures the deviation of the locally estimated optical flow from the corresponding global motion. The second method computes a change detection mask by taking the difference between consecutive frames. The first version is more suitable for sequences with little motion, whereas the second version is better at dealing with faster moving or changing objects. Experimental results demonstrate the performance of our algorithm