Using Perceptual Organization to Extract 3D Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental computation of planar maps
SIGGRAPH '89 Proceedings of the 16th annual conference on Computer graphics and interactive techniques
3-D Shape Recovery Using Distributed Aspect Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Adaptive vectorization of line drawing images
Computer Vision and Image Understanding
Parametric Model Fitting: From Inlier Characterization to Outlier Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Veinerization: A New Shape Description for Flexible Skeletonization
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Recursive, O(N) Partitioning of a Digitized Curve into Digital Straight Segments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vanishing Point Detection by Line Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A topologically consistent representation for image analysis: the Topological Graph of Frontiers
DCGA '96 Proceedings of the 6th International Workshop on Discrete Geometry for Computer Imagery
Mustererkennung 1995, 17. DAGM-Symposium
Polymorphic grouping for image segmentation
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Introduction to Combinatorial Pyramids
Digital and Image Geometry, Advanced Lectures [based on a winter school held at Dagstuhl Castle, Germany in December 2000]
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Many authors have already proposed linear feature extraction algorithms. In most cases, these algorithms can not guarantee the extraction of adjacency relations between extracted features. Object contours appearing in the analyzed images are often fragmented into nonconnected features. Nevertheless, the use of some topological information enables to reduce substantially the complexity of matching and registration algorithms. Here, we formulate the problem of linear feature extraction as an optimal labelling problem of a topological map obtained from low level operations. The originality of our approach is the maintaining of this data structure during the extraction process and the formulation of the problem of feature extraction as a global optimization problem.