Robust regression and outlier detection
Robust regression and outlier detection
Artificial Intelligence
International Journal of Computer Vision
Bayesian Estimation of Motion Vector Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine vision
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Introduction to the Special Section on Digital Libraries: Representation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
VideoQ: an automated content based video search system using visual cues
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measurement of Visual Motion
Using geometric corners to build a 2D mosaic from a set of image
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Slow and Smooth: A Bayesian theory for the combination of local motion signals in human vision
Slow and Smooth: A Bayesian theory for the combination of local motion signals in human vision
Multiresolution Gauss-Markov random field models for texture segmentation
IEEE Transactions on Image Processing
Motion segmentation by multistage affine classification
IEEE Transactions on Image Processing
Dense estimation and object-based segmentation of the optical flow with robust techniques
IEEE Transactions on Image Processing
Color object indexing and retrieval in digital libraries
IEEE Transactions on Image Processing
The application of mean field theory to image motion estimation
IEEE Transactions on Image Processing
An efficient two-pass MAP-MRF algorithm for motion estimation based on mean field theory
IEEE Transactions on Circuits and Systems for Video Technology
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In our earlier work, a two-pass motion estimation algorithm (TPA) was developed to estimate a motion field for two adjacent frames in an image sequence where contextual constraints are handled by several Markov random fields (MRFs) and the maximum a posteriori (MAP) configuration is taken to be the resulting motion field. In order to provide a trade-off between efficiency and effectiveness, the mean field theory (MFT) was selected to carry out the optimization process to locate the MAP with desirable performance. Given that currently in the disciplines of digital library [IEEE Trans. PAMI 18 (8) (1996); IEEE Trans. Image Process. 11 (8) (2002) 912] and video processing [IEEE Trans. Circ. Sys. Video Tech. 7 (1) (1997)] of utmost interest are the extraction and representation of visual objects, instead of estimating motion field, in this paper we focus on segmenting out visual objects based on spatial and temporal properties present in two contiguous frames in the same MRF-MAP-MFT framework. To achieve object segmentation, a "motion boundary field" is introduced which can turn off interactions between different object regions and in the mean time remove spurious object boundaries. Furthermore, in light of the generally smooth and slow velocities in-between two contiguous frames, we discover that in the process of calculating matching blocks, assigning different weights to different locations can result in better object segmentation. Experimental results conducted on both synthetic and real-world videos demonstrate encouraging performance.