2D Shape Recognition Using Synthesized Affine Invariant Function with Starting Point Invariance
Date Issued
2009
Date
2009
Author(s)
Guo, Bo-Yien
Abstract
Affine invariant function (AIF) which is independent of affine transformations such as rotation, translation, scaling, and skewing, is a useful tool of shape recognition. Synthesized Affine Invariant Function (SAIF) which receives synthesized feature signals to determine the parametric curve features no information loss in representing the shape for recognition. While SAIF can represent the detailed contour signals, it is very sensitive to the starting point of the contour signal. By taking the advantages of the uniqueness of natural axis, the invariance of contour skeletonization, and the localization furthest centroid distance of contour, this thesis proposes the constrained natural axis method. The constrained natural axis under almost all conditions can determine a unique starting point on the contour; in turn the SAIF curve refers to the starting point is unique. That makes shape recognition based on SAIF practical and useful. Experimental results show and confirm the feasibility of the innovative method.
Subjects
Shape recognition
affine invariant function
constrained natural axis
wavelet analysis
Type
thesis
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