Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
MAP-Based Stochastic Diffusion for Stereo Matching and Line Fields Estimation
International Journal of Computer Vision
Color image segmentation using fuzzy C-means and eigenspace projections
Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametric model-based motion segmentation using surface selection criterion
Computer Vision and Image Understanding
Spatio-temporal video object segmentation via scale-adaptive 3D structure tensor
EURASIP Journal on Applied Signal Processing
Motion representation using composite energy features
Pattern Recognition
A Block Based Moving Object Detection Utilizing the Distribution of Noise
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Region-level motion-based foreground segmentation under a Bayesian network
IEEE Transactions on Circuits and Systems for Video Technology
Parametric model-based motion segmentation using surface selection criterion
Computer Vision and Image Understanding
Block-based motion field segmentation for video coding
Journal of Visual Communication and Image Representation
A Bayesian network for foreground segmentation in region level
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
A new approach for vehicle detection in congested traffic scenes based on strong shadow segmentation
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Approximation algorithm for the kinetic robust K-center problem
Computational Geometry: Theory and Applications
OSCAR: object segmentation using correspondence and relaxation
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
A novel approach to FRUC using discriminant saliency and frame segmentation
IEEE Transactions on Image Processing
Object segmentation in videos from moving camera with MRFs on color and motion features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Bayesian method for motion segmentation and tracking in compressed videos
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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
New optical flow approach for motion segmentation based on gamma distribution
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Motion detection using wavelet analysis and hierarchical markov models
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
Motion Coherent Tracking Using Multi-label MRF Optimization
International Journal of Computer Vision
A novel video salient object extraction method based on visual attention
Image Communication
Accurate image registration for MAP image super-resolution
Image Communication
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We present a Bayesian framework that combines motion (optical flow) estimation and segmentation based on a representation of the motion field as the sum of a parametric field and a residual field. The parameters describing the parametric component are found by a least squares procedure given the best estimates of the motion and segmentation fields. The motion field is updated by estimating the minimum-norm residual field given the best estimate of the parametric field, under the constraint that motion field be smooth within each segment. The segmentation field is updated to yield the minimum-norm residual field given the best estimate of the motion field, using Gibbsian priors. The solution to successive optimization problems are obtained using the highest confidence first (HCF) or iterated conditional mode, (ICM) optimization methods. Experimental results on real video are shown