Three-dimensional object recognition
ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
A 3-D Contour Segmentation Scheme Based on Curvature and Torsion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Range Image Segmentation Based on Differential Geometry: A Hybrid Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing 3-D Objects Using Surface Descriptions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation and recognition of surface shapes in range images: a differential geometry approach
Computer Vision, Graphics, and Image Processing
Polygonal shape recognition using string-matching techniques
Pattern Recognition
Using Extremal Boundaries for 3-D Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Smoothing and matching of 3-D space curves
International Journal of Computer Vision
Measurement of 3D-line shaped objects
VIP '94 The international conference on volume image processing on Volume image processing
Recognition of 2D Object Contours Using the Wavelet Transform Zero-Crossing Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The String-to-String Correction Problem
Journal of the ACM (JACM)
Computation of Normalized Edit Distance and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconstruction of 3-D Binary Tree-Like Structures From Three Mutually Orthogonal Projections
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computation of Surface Geometry and Segmentation Using Covariance Techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A method to construct a representation of space curves based on the zero-crossings of the dyadic wavelet transform is introduced. The principal axes of inertia of these space curves, referred to as objects, are considered as the reference system. The representation is translation, rotation and size invariant. Instances of objects in images are recognised by matching their representations with those of the models. A string-matching technique is adapted and used for this purpose. Experimental results show that the representation is robust and efficient in extracting and matching object information.