Video sequence segmentation using genetic algorithms
Pattern Recognition Letters
MAP-Based Stochastic Diffusion for Stereo Matching and Line Fields Estimation
International Journal of Computer Vision
Video Object Extraction for Object-Oriented Applications
Journal of VLSI Signal Processing Systems
Signal Processing - Signal processing with heavy-tailed models
Extracting Semantic Video Objects
IEEE Computer Graphics and Applications
Video segmentation using fast marching and region growing algorithms
EURASIP Journal on Applied Signal Processing - Image analysis for multimedia interactive services - part I
A fast automatic VOP generation using boundary block segmentation
Real-Time Imaging
Extraction of video object with complex motion
Pattern Recognition Letters
Hybrid Morphology Processing Unit Architecture for Moving Object Segmentation Systems
Journal of VLSI Signal Processing Systems
Automatic video segmentation using genetic algorithms
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Compression of Patient Monitoring Video Using Motion Segmentation Technique
Journal of Medical Systems
Video segmentation using fast marching and region growing algorithms
EURASIP Journal on Applied Signal Processing
A standard-compliant virtual meeting system with active video object tracking
EURASIP Journal on Applied Signal Processing
An application of MAP-MRF to change detection in image sequence based on mean field theory
EURASIP Journal on Applied Signal Processing
Interaction between high-level and low-level image analysis for semantic video object extraction
EURASIP Journal on Applied Signal Processing
Spatio-temporal video object segmentation via scale-adaptive 3D structure tensor
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
Motion segmentation using Markov random field model for accurate moving object segmentation
Proceedings of the 2nd international conference on Ubiquitous information management and communication
A novel background updating algorithm based on the logical relationship
SSIP'07 Proceedings of the 7th WSEAS International Conference on Signal, Speech and Image Processing
Automatic Segmentation of Non-rigid Objects in Image Sequences Using Spatiotemporal Information
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Semi-automatic video object segmentation using seeded region merging and bidirectional projection
Pattern Recognition Letters
Automatic object-based video segmentation using distributed genetic algorithms
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartI
Scalable object-based video retrieval in HD video databases
Image Communication
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
Entropy based region selection for moving object detection
Pattern Recognition Letters
Constrained region-growing and edge enhancement towards automated semantic video object segmentation
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Segmentation of the liver using the deformable contour method on CT images
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
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The new MPEG-4 video coding standard enables content-based functionalities. In order to support the philosophy of the MPEG-4 visual standard, each frame of video sequences should be represented in terms of video object planes (VOPs). In other words, video objects to be encoded in still pictures or video sequences should be prepared before the encoding process starts. Therefore, it requires a prior decomposition of sequences into VOPs so that each VOP represents a moving object. This paper addresses an image segmentation method for separating moving objects from the background in image sequences. The proposed method utilizes the following spatio-temporal information. (1) For localization of moving objects in the image sequence, two consecutive image frames in the temporal direction are examined and a hypothesis testing is performed by comparing two variance estimates from two consecutive difference images, which results in an F-test. (2) Spatial segmentation is performed to divide each image into semantic regions and to find precise object boundaries of the moving objects. The temporal segmentation yields a change detection mask that indicates moving areas (foreground) and nonmoving areas (background), and spatial segmentation produces spatial segmentation masks. A combination of the spatial and temporal segmentation masks produces VOPs faithfully. This paper presents various experimental results