On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Accuracy of Zernike Moments for Image Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Image analysis by Tchebichef moments
IEEE Transactions on Image Processing
Rotation-invariant multiresolution texture analysis using Radon and wavelet transforms
IEEE Transactions on Image Processing
A high performance parallel Radon based OFDM transceiver design and simulation
Digital Signal Processing
Journal of Visual Communication and Image Representation
The RST invariant digital image watermarking using Radon transforms and complex moments
Digital Signal Processing
Quaternion Fourier-Mellin moments for color images
Pattern Recognition
Robust matching of multi-modal retinal images using radon transform based local descriptor
Proceedings of the 1st ACM International Health Informatics Symposium
Fast computation of orthogonal Fourier---Mellin moments in polar coordinates
Journal of Real-Time Image Processing
A N-Radon Based OFDM Trasceivers Design and Performance Simulation Over Different Channel Models
Wireless Personal Communications: An International Journal
Invariant pattern recognition using the RFM descriptor
Pattern Recognition
The generalization of the R-transform for invariant pattern representation
Pattern Recognition
Computers & Mathematics with Applications
Object recognition using radon transform-based RST parameter estimation
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Invariant object recognition using radon and fourier transforms
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Off-line hand written input based identity determination using multi kernel feature combination
Pattern Recognition Letters
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Various types of orthogonal moments have been widely used for object recognition and classification. However, these moments do not natively possess scaling invariance, essential image normalization and binarization prior to moments extraction will lead to error of resampling and requantifying. This paper describes a new scaling and rotation invariant analysis method for object recognition. In the proposed method, the Radon transform is utilized to project the image onto projection space to convert the rotation of the original image to a translation of the projection in the angle variable and the scaling of the original image to a scaling of the projection in the spatial variable together with an amplitude scaling of the projection, and then the Fourier-Mellin transform is applied to the result to convert the translation in the angle variable and the scaling in the spatial variable as well as the amplitude scaling of the projection to a phase shift and an amplitude scaling, respectively. In order to achieve a set of completely invariant descriptors, a rotation and scaling invariant function is constructed. A k-nearest neighbors' classifier is employed to implement classification. Theoretical and experimental results show the high classification accuracy of this approach as a result of using the rotation and scaling invariant function instead of image binarization and normalization, it is also shown that this method is relatively robust in the presence of white noise.