Anchorperson extraction for picture in picture news video
Pattern Recognition Letters
A motion-flow-based fast video retrieval system
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Real-time spatiotemporal segmentation of video objects in the H.264 compressed domain
Journal of Visual Communication and Image Representation
Compression of Patient Monitoring Video Using Motion Segmentation Technique
Journal of Medical Systems
Interaction between high-level and low-level image analysis for semantic video object extraction
EURASIP Journal on Applied Signal Processing
Applying the multi-category learning to multiple video object extraction
Pattern Recognition
SEGMENTATION OF MULTIPLE HUMAN OBJECTS IN VIDEO SEQUENCES
Applied Artificial Intelligence
Region-level motion-based foreground segmentation under a Bayesian network
IEEE Transactions on Circuits and Systems for Video Technology
Trajectory tree as an object-oriented hierarchical representation for video
IEEE Transactions on Circuits and Systems for Video Technology
GVE '07 Proceedings of the IASTED International Conference on Graphics and Visualization in Engineering
Taxonomy of directing semantics for film shot classification
IEEE Transactions on Circuits and Systems for Video Technology
Semi-automatic video object segmentation using seeded region merging and bidirectional projection
Pattern Recognition Letters
A Bayesian network for foreground segmentation in region level
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Automatic moving object segmentation from video sequences using alternate flashing system
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
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
Small object detection and tracking: algorithm, analysis and application
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Moving object segmentation: a block-based moving region detection approach
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
Region-Level motion-based foreground detection with shadow removal using MRFs
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Motion field refinement and region-based motion segmentation
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
Extracting representative motion flows for effective video retrieval
Multimedia Tools and Applications
An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation
Computer Vision and Image Understanding
Layered moving-object segmentation for stereoscopic video using motion and depth information
Journal of Visual Communication and Image Representation
Hi-index | 0.00 |
The emerging video coding standard MPEG-4 enables various content-based functionalities for multimedia applications. To support such functionalities, as well as to improve coding efficiency, MPEG-4 relies on a decomposition of each frame of an image sequence into video object planes (VOP). Each VOP corresponds to a single moving object in the scene. This paper presents a new method for automatic segmentation of moving objects in image sequences for VOP extraction. We formulate the problem as graph labeling over a region adjacency graph (RAG), based on motion information. The label field is modeled as a Markov random field (MRF). An initial spatial partition of each frame is obtained by a fast, floating-point based implementation of the watershed algorithm. The motion of each region is estimated by hierarchical region matching. To avoid inaccuracies in occlusion areas, a novel motion validation scheme is presented. A dynamic memory, based on object tracking, is incorporated into the segmentation process to maintain temporal coherence of the segmentation. Finally, a labeling is obtained by maximization of the a posteriori probability of the MRF using motion information, spatial information and the memory. The optimization is carried out by highest confidence first (HCF). Experimental results for several video sequences demonstrate the effectiveness of the proposed approach