IEEE Transactions on Pattern Analysis and Machine Intelligence
General methods for determining projective invariants in imagery
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Invariant Descriptors for 3D Object Recognition and Pose
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Appendix—projective geometry for machine vision
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Geometric invariants and object recognition
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CVGIP: Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Describing Complicated Objects by Implicit Polynomials
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Recognizing Planar Objects Using Invariant Image Features
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Noise-Resistant Invariants of Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Recognizing Articulated Objects Using a Region-Based Invariant Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Robust symbolic representation for shape recognition and retrieval
Pattern Recognition
Robust symbolic representation for shape recognition and retrieval
Pattern Recognition
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Digital Signal Processing
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Iterative 3D point-set registration based on hierarchical vertex signature (HVS)
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Shape categorization using string kernels
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Unsupervised clustering of shapes
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Geometric invariants are shape descriptors that remain unchanged under geometric transformations such as projection or changing the viewpoint. A new method of obtaining local projective and affine invariants is developed and implemented for real images. Being local, the invariants are much less sensitive to occlusion than global invariants. The invariants驴 computation is based on a canonical method. This consists of defining a canonical coordinate system by the intrinsic properties of the shape, independently of the given coordinate system. Since this canonical system is independent of the original one, it is invariant and all quantities defined in it are invariant. The method was applied without the use of a curve parameter. This was achieved by fitting an implicit polynomial to an arbitrary curve in a vicinity of each curve point. Several configurations are treated: a general curve without any correspondence and curves with known correspondences of one or two feature points or lines. Experimental results for different 2D objects in 3D space are presented.