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This work provides quantitative analysis of error sensitivity of 3-D shape and motion estimation subject to small feature correspondence errors.
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Grayscale histogram with infrared images is used for feature correspondence.
Based on the feature correspondence, the measurement surface is localized very close to the design model.
Ribisi's small features are amazingly expressive.
(c) Counting individual local feature correspondences in input images.
The spatial map between scattered local feature correspondences and structured landmark correspondences is learned via Kernel Ridge Regression (KRR).
In each of feature region seeking process, the number of feature correspondences is registered.
The deformation is generated automatically without any user intervention or specification of feature correspondences.
The differential approaches use differential optical flow whereas the discrete approaches use feature correspondences.
The crucial step is to establish proper feature correspondences between a large number of input models.
Since true correspondences result in good registration and vice versa, the verification of feature correspondences can be carried out through evaluating their registration results.
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