Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
The revised Fundamental Theorem of Moment Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generation of moment invariants and their uses for character recognition
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Degraded Image Analysis: An Invariant Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient Method for the Computation of Legendre Moments
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
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
Radial and Angular Moment Invariants for Image Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Normalization by Complex Moments
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
Fast computation of accurate Gaussian-Hermite moments for image processing applications
Digital Signal Processing
A complete set of pseudo-zernike moment invariants by image shape description
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
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Geometric moment invariants are widely used in many fields of image analysis and pattern recognition since their first introduction by Hu in 1962. A few years ago, Flusser has proved how to find the independent and complete set of geometric moment invariants corresponding to a given order. On the other hand, the properties of orthogonal moments show that they can be recognized as useful tools for image representation and reconstruction. Therefore, derivation of invariants from orthogonal moments becomes an interesting subject and some results have been reported in literature. In this paper, we propose to use a family of orthogonal moments, called Gaussian-Hermite moments and defined with Hermite polynomials, for deriving their corresponding invariants. The rotation invariants of Gaussian-Hermite moments can be achieved algebraically according to a property of Hermite polynomials. This approach is definitely different from the conventional methods which derive orthogonal moment invariants either by image normalization or by an expression as a linear combination of the invariants of geometric moments. One significant conclusion drawn is that the rotation invariants of Gaussian-Hermite moments have the identical forms to those of geometric moments. This coincidence is also proved mathematically in the appendix of the paper. Moreover, the translation invariants could be easily constructed by translating the coordinate origin to the image centroid. The invariants of Gaussian-Hermite moments both to rotation and to translation are accomplished by the combination of these two kinds of invariants. Their rotational and translational invariance is evaluated by a set of transformed gray-level images. The numeric stabilities of the proposed invariant descriptors are also discussed under both noise-free and noisy conditions. The computational complexity and time for implementing such invariants are analyzed as well. In addition to this, the better performance of the Gaussian-Hermite invariants is experimentally demonstrated by pattern matching in comparison with geometric moment invariants.