Detection of generalized principal axes is rotationally symmetric shapes
Pattern Recognition
Computing a shape's moments from its boundary
Pattern Recognition
Simple and fast computation of moments
Pattern Recognition
Machine vision
Multigrid Convergence of Calculated Features in Image Analysis
Journal of Mathematical Imaging and Vision
Aircraft identification by moment invariants
IEEE Transactions on Computers
Comparison and improvement of tangent estimators on digital curves
Pattern Recognition
Experimental comparison of continuous and discrete tangent estimators along digital curves
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
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Moment-based procedures are commonly used in computer vision, image analysis, or pattern recognition Basic shape features such as size, position, orientation, or elongation are estimated by moments of order ≤2 Shape invariants are defined by higher order moments In contrast to a theory of moments in continuous mathematics, shape moments in imaging have to be estimated from digitized data Infinitely many different shapes in Euclidean space are represented by an identical digital shape There is an inherent loss of information, impacting moment estimation. This paper discusses accuracy limitations in moment reconstruction in dependency of order of reconstructed moments and applied resolution of digital pictures We consider moments of arbitrary order, which is not assumed to be bounded by a constant.