Multiresolution object detection and delineation
Computer Vision, Graphics, and Image Processing
Multiple Resolution Representation and Probabilistic Matching of 2-D Gray-Scale Shape
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
The Translation Sensitivity of Wavelet-Based Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
The evaluation of normalized cross correlations for defect detection
Pattern Recognition Letters
A new use of the ridgelets transform for describing linear singularities in images
Pattern Recognition Letters
Product quality on-line inspecting for the pressed protuberant character on a metal tag
Image and Vision Computing
Efficient feature correspondence for image registration
SSIP'06 Proceedings of the 6th WSEAS International Conference on Signal, Speech and Image Processing
Non-Iterative Hierarchical Registration for Medical Images
Journal of Signal Processing Systems
A novel Fourier descriptor based image alignment algorithm for automatic optical inspection
Journal of Visual Communication and Image Representation
Analysis of a Plurality Voting-based Combination of Classifiers
Neural Processing Letters
An eigenvalue-based similarity measure and its application in defect detection
Image and Vision Computing
Image matching using distance transform
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Multi-oriented Bangla and Devnagari text recognition
Pattern Recognition
Mid-level smoke control for 2D animation
Proceedings of Graphics Interface 2011
A scaling and rotating invariant object matching algorithm
Mathematical and Computer Modelling: An International Journal
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In this paper, we propose a wavelet decomposition approach for rotation-invariant template matching. In the matching process, we first decompose an input image into different multi-resolution levels in the wavelet-transformed domain, and use only the pixels with high wavelet coefficients in the decomposed detail subimage at a lower resolution level to compute the normalized correlation between two compared patterns. To make the matching invariant to rotation, we further use the ring-projection transform, which is invariant to object orientation, to represent an object pattern in the detail subimage. The proposed method significantly reduces the computational burden of the traditional pixel-by-pixel matching. Experimental results on a variety of real images have shown the efficacy of the proposed method.