Using Robust Estimation Algorithms for Tracking Explicit Curves
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Data-Driven Extraction of Curved Intersection Lanemarks from Road Traffic Image Sequences
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Concatenate feature extraction for robust 3D elliptic object localization
Proceedings of the 2004 ACM symposium on Applied computing
Salient feature extraction of industrial objects for an automated assembly system
Computers in Industry - Special issue: Machine vision
Salient feature extraction of industrial objects for an automated assembly system
Computers in Industry
Backward segmentation and region fitting for geometrical visibility range estimation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Pattern Recognition Letters
SBM'06 Proceedings of the Third Eurographics conference on Sketch-Based Interfaces and Modeling
From paper to machine: extracting strokes from images for use in sketch recognition
SBM'08 Proceedings of the Fifth Eurographics conference on Sketch-Based Interfaces and Modeling
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We present an algorithm that extracts curves from a set of edgels within a specific class in a decreasing order of their "length". The algorithm inherits the perceptual grouping approaches. But, instead of using only local cues, a global constraint is imposed to each extracted subset of edgels, that the underlying curve belongs to a specific class.In order to reduce the complexity of the solution, we work with a linearly parameterized class of curves, function of one image coordinate. This allows, first, to use a recursive Kalman based fitting and, second, to cast the problem as an optimal path search in an directed graph. Experiments on finding lane-markings on roads demonstrate that real-time processing is achievable.