Scale-Space for Discrete Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cooperative Robust Estimation Using Layers of Support
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Parallel Computation of Stochastic Completion Fields
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Region-Based Affine Motion Segmentation Using Color Information
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Unsupervised video object segmentation and tracking based on new edge features
Pattern Recognition Letters
Video object segmentation and tracking using region-based statistics
Image Communication
Robust Real-Time Bi-Layer Video Segmentation Using Infrared Video
CRV '08 Proceedings of the 2008 Canadian Conference on Computer and Robot Vision
Performance measures for video object segmentation and tracking
IEEE Transactions on Image Processing
Unsupervised video segmentation based on watersheds and temporal tracking
IEEE Transactions on Circuits and Systems for Video Technology
Video object tracking with feedback of performance measures
IEEE Transactions on Circuits and Systems for Video Technology
Robust segmentation and tracking of colored objects in video
IEEE Transactions on Circuits and Systems for Video Technology
Video object segmentation using Bayes-based temporal tracking and trajectory-based region merging
IEEE Transactions on Circuits and Systems for Video Technology
Automatic moving object extraction for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
A Bayesian approach to video object segmentation via merging 3-D watershed volumes
IEEE Transactions on Circuits and Systems for Video Technology
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This paper proposes an unsupervized offline video object segmentation method that introduces a number of improvements to existing work in the area. It consists of the following steps. The initial segmentation utilizes object color and motion variance to more accurately classify image pixels in the first frame. Histogram-based merging is then employed to reduce oversegmentation of the first frame. During object tracking, segmentation quality measures based on object color and motion contrast are taken. These measures are then used to enhance video objects through selective pixel reclassification. After object enhancement, cumulative histogram-based merging, occlusion handling, and island detection are used to help group regions into meaningful objects. Compared to two reference methods, greater success and improved accuracy in segmenting video objects are first demonstrated by subjectively examining selected frames from a set of standard video sequences. Objective results are obtained through the use of a set of measures that aim at evaluating the accuracy of object boundaries and temporal stability through the use of color, motion, and histograms.