Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of Affine-Invariant Fourier Descriptors to Recognition of 3-D Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Contour Extraction Using Adaptive B-Snake Model
Journal of Mathematical Imaging and Vision
Dynamic B-snake model for complex objects segmentation
Image and Vision Computing
Affine-invariant B-spline moments for curve matching
IEEE Transactions on Image Processing
Invariant matching and identification of curves using B-splines curve representation
IEEE Transactions on Image Processing
Pattern Recognition Letters
Robust curve clustering based on a multivariate t-distribution model
IEEE Transactions on Neural Networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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This paper presents a new affine-invariant matching algorithm based on B-Spline modeling, which solves the problem of the non-uniqueness of B-Spline in curve matching. This method first smoothes the B-Spline curve by increasing the degree of the curve. It is followed by a reduction of the curve degree using the Least Square Error (LSE) approach to construct the Curvature Scale Space (CSS) image. CSS matching is then carried out. Our method combines the advantages of B-Spline that are continuous curve representation and the robustness of CSS matching with respect to noise and affine transformation. It avoids the need for other matching algorithms that have to use the re-sampled points on the curve. Thus, the curve matching error is reduced. The proposed algorithm has been tested by matching similar shapes from a prototype database. The experimental results showed the robustness and accuracy of the proposed method in B-Spline curve matching.