Finding Trajectories of Feature Points in a Monocular Image Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene Segmentation from Visual Motion Using Global Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV 90 Proceedings of the first european conference on Computer vision
International Journal of Computer Vision
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Motion segmentation and qualitative dynamic scene analysis from an image sequence
International Journal of Computer Vision
Region-based tracking using affine motion models in long image sequences
CVGIP: Image Understanding
Computer and Robot Vision
Combining Intensity and Motion for Incremental Segmentation and Tracking Over Long Image Sequences
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Spatio-Temporal Robust Motion Estimation and Segmentation
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Video object segmentation
An FFT-based technique for translation, rotation, and scale-invariant image registration
IEEE Transactions on Image Processing
Self-affine mapping system and its application to object contour extraction
IEEE Transactions on Image Processing
A fully automated content-based video search engine supporting spatiotemporal queries
IEEE Transactions on Circuits and Systems for Video Technology
Image sequence analysis for emerging interactive multimedia services-the European COST 211 framework
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
Automatic detection of salient objects and spatial relations in videos for a video database system
Image and Vision Computing
What can we learn from biological vision studies for human motion segmentation?
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Extracting moving / static objects of interest in video
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
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We introduce an automatic segmentation framework that blends the advantages of color-, texture-, shape-, and motion-based segmentation methods in a computationally feasible way. A spatiotemporal data structure is first constructed for each group of video frames, in which each pixel is assigned a feature vector based on low-level visual information. Then, the smallest homogeneous components, so-called volumes, are expanded from selected marker points using an adaptive, three-dimensional, centroid-linkage method. Self descriptors that characterize each volume and relational descriptors that capture the mutual properties between pairs of volumes are determined by evaluating the boundary, trajectory, and motion of the volumes. These descriptors are used to measure the similarity between volumes based on which volumes are further grouped into objects. A fine-to-coarse clustering algorithm yields a multiresolution object tree representation as an output of the segmentation.