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Optimized multi-scale affine shape registration based on an unsupervised Bayesian classification
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AI
DOI：10.1007/s11042-023-14890-4.png)
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Here, we intend to introduce an efficient, robust curve alignment algorithm with respect to the group of special affine transformations of the plane denoted by SA(2,R). Such a group of transformations is known to be well model the pose of 3D scene when objects are far from the visual sensor relatively to their seizes. Its numerical robustness lies in its multi-scale approach and its precision comes from the automatic and unsupervised Bayesian selection of the efficient scales in the sens of L-2 metric. In this work, We prove its high alignment performance on the most studied image databases such as MPEG-7, MCD, Kimia-99, Kimia216, ETH-80, and the Swedish leaf experimentally. The unsupervised Bayesian classification is based on the well-known multiclass Expectation-Maximization algorithm.
Keyword:
Affine transformations
A normalised affine arc-length parametrization
ACM Algorithm
Multi-scale registrations
Multiclass Expectation Maximisation (Multiclass-EM)
Unsupervised bayesian classification
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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