A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
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
Geometric invariants and object recognition
International Journal of Computer Vision
An application of wavelet-based affine-invariant representation
Pattern Recognition Letters
Wavelet-Based Affine Invariant Representation: A Tool for Recognizing Planar Objects in 3D Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric and Illumination Invariants for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Dyadic Wavelet Affine Invariant Function for 2D Shape Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Affine invariants for object recognition using the wavelet transform
Pattern Recognition Letters
Affine-invariant B-spline moments for curve matching
IEEE Transactions on Image Processing
Bio-inspired Connectionist Architecture for Visual Detection and Refinement of Shapes
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
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By defining the weighted wavelet synthesis, the synthesized feature signals of an interesting shape are extracted to derive the innovative synthesized affine invariant function (SAIF). The synthesized feature signals hold the shape information with minimum loss by excluding simply the translation dependent and noise-contaminated bands. The SAIF is shown excellent in the invariance property and representative in describing the original shape for automated recognition. Experimental results demonstrate that automated shape recognition based on the SAIF achieves high correctness and significantly outperforms those using conventional wavelet affine invariant functions.