On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Range Image Segmentation Based on Differential Geometry: A Hybrid Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D Moment Forms: Their Construction and Application to Object Identification and Positioning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moment-preserving corner detection
Pattern Recognition
Pattern recognition with moment invariants: a comparative study and new results
Pattern Recognition
Moment-based texture segmentation
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Traffic object detections and its action analysis
Pattern Recognition Letters
Image analysis by discrete orthogonal Racah moments
Signal Processing
Translation and scale invariants of Tchebichef moments
Pattern Recognition
Image analysis by discrete orthogonal dual Hahn moments
Pattern Recognition Letters
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
Fast computation of geometric moments using a symmetric kernel
Pattern Recognition
Refined translation and scale Legendre moment invariants
Pattern Recognition Letters
Recognitive Aspects of Moment Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image analysis by Tchebichef moments
IEEE Transactions on Image Processing
Image analysis by Krawtchouk moments
IEEE Transactions on Image Processing
Some computational aspects of discrete orthonormal moments
IEEE Transactions on Image Processing
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Orthogonal moments are powerful tools in pattern recognition and image processing applications. In this paper, the Gaussian-Hermite moments based on a set of orthonormal weighted Hermite polynomials are extensively studied. The rotation and translation invariants of Gaussian-Hermite moments are derived algebraically. It is proved that the construction forms of geometric moment invariants are valid for building the Gaussian-Hermite moment invariants. The paper also discusses the computational aspects of Gaussian-Hermite moment, including the recurrence relation and symmetrical property. Just as the other orthogonal moments, an image can be easily reconstructed from its Gaussian-Hermite moments thanks to the orthogonality of the basis functions. Some reconstruction tests with binary and gray-level images (without and with noise) were performed and the obtained results show that the reconstruction quality from Gaussian-Hermite moments is better than that from known Legendre, discrete Tchebichef and Krawtchouk moments. This means Gaussian-Hermite moment has higher image representation ability. The peculiarity of image reconstruction algorithm from Gaussian-Hermite moments is also discussed in the paper. The paper offers an example of classification using Gaussian-Hermite moment invariants as pattern feature and the result demonstrates that Gaussian-Hermite moment invariants perform significantly better than Hu's moment invariants under both noise-free and noisy conditions.