On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Traffic object detections and its action analysis
Pattern Recognition Letters
Fast Zernike wavelet moments for Farsi character recognition
Image and Vision Computing
Application of a new type of singular points in fingerprint classification
Pattern Recognition Letters
Exact Legendre moment computation for gray level images
Pattern Recognition
Properties of orthogonal Gaussian-Hermite moments and their applications
EURASIP Journal on Applied Signal Processing
Some Aspects of Gaussian-Hermite Moments in Image Analysis
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Subpixel edge location based on orthogonal Fourier-Mellin moments
Image and Vision Computing
Orthogonal moments based texture analysis of CT liver images
Pattern Recognition Letters
Fast and accurate method for radial moment's computation
Pattern Recognition Letters
A systematic method for efficient computation of full and subsets Zernike moments
Information Sciences: an International Journal
Image representation using accurate orthogonal Gegenbauer moments
Pattern Recognition Letters
Rotation and translation invariants of Gaussian-Hermite moments
Pattern Recognition Letters
Fingerprint image segmentation based on gaussian-hermite moments
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Bayesian Wavelet-Based Image Denoising Using the Gauss–Hermite Expansion
IEEE Transactions on Image Processing
The fast recursive computation of Tchebichef moment and its inverse transform based on Z-transform
Digital Signal Processing
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Gaussian-Hermite moments are orthogonal moments widely used in image processing and computer vision applications. Similar to the other families of orthogonal moments, highly computational demands represent the main challenging. In this work, an efficient method is proposed for fast computation of highly accurate Gaussian-Hermite moments for gray-level images. The proposed method achieves the accuracy through the integration of Gaussian-Hermite polynomials over the image pixels. To achieve the efficiency, the symmetry property of Gaussian-Hermite polynomials is employed where the computational complexity is reduced by 75%. Fast computational methodology is employed to significantly accelerate the computational process where the 2D Gaussian-Hermite moments are treated in a separated form. Numerical experiments are performed where the results are compared with the conventional method. The comparison of the obtained results clearly ensures the efficiency of the proposed method.