A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A computational approach for corner and vertex detection
International Journal of Computer Vision
Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Computable elastic distances between shapes
SIAM Journal on Applied Mathematics
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective Tracking through Tree-Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISCV '95 Proceedings of the International Symposium on Computer Vision
Visual mapping by a robot rover
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
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We present a method for figure-ground segregation of moving objects from monocular video sequences. The approach is based on tracking extracted contour fragments, in contrast to traditional approaches which rely on feature points, regions, and unorganized edge elements. Specifically, a notion of similarity between pairs of curve fragments appearing in two adjacent frames is developed and used to find the curve correspondence. This similarity metric is elastic in nature and in addition takes into account both a novel notion of transitions in curve fragments across video frames and an epipolar constraint. This yields a performance rate of 85% correct correspondence on a manually labeled set of frame pairs. The retrieved curve correspondence is then used to group curves in each frame into clusters based on the pairwise similarity of how they transform from one frame to the next. Results on video sequences of moving vehicles show that using curve fragments for tracking produces a richer segregation of figure from ground than current region or feature-based methods.