Three-dimensional object recognition
ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
Fundamentals of interactive computer graphics
Fundamentals of interactive computer graphics
The representation, recognition, and locating of 3-d objects
International Journal of Robotics Research
HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
New methods for matching 3-D objects with single perspective views
IEEE Transactions on Pattern Analysis and Machine Intelligence
Localizing Overlapping Parts by Searching the Interpretation Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition and localization via pose clustering
Computer Vision, Graphics, and Image Processing
Maximal matching of 3-D points for multiple-object motion estimation
Pattern Recognition
Robot Vision
Pose Determination from Line-to-Plane Correspondences: Existence Condition and Closed-Form Solutions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Sets of 3D Line Segments with Application to Polygonal Arc Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dual Computation of Projective Shape and Camera Positions from Multiple Images
International Journal of Computer Vision
Optic Flow Field Segmentation and Motion Estimation Using a Robust Genetic Partitioning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Matching 3D Line Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Vision Computing
An invariant, closed-from solution for matching sets of 3D lines
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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A two-stage algorithm for matching line segments using three-dimensional data is presented. In the first stage, a tree-search based on the orientation of the line segments is applied to establish potential matches. the sign ambiguity of line segments is fixed by a simple congruency constraint. In the second stage, a Hough clustering technique based on the position of line segments is applied to verify potential matches. Any paired line segments of a match that cannot be brought to overlap by the translation determined by the clustering are removed from the match. Unlike previous methods, this algorithm combats noise more effectively, and ensures the global consistency of a match. While the original motivation for the algorithm is multiple-object motion estimation from stereo image sequences, the algorithm can also be applied to other domains, such as object recognition and object model construction from multiple views.