Horny fem Le Fauga

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As forgeries have become popular, the importance of forgery detection is much increased. Copy-move forgery, one of the most commonly used methods, copies a part of the image and pastes it into another part of the the image. In this paper, we propose a detection method of copy-move forgery that localizes duplicated regions using Zernike moments.

Since the magnitude of Zernike moments is algebraically invariant against rotation, the proposed method can detect a forged region even though it is rotated. Our scheme is also resilient to the intentional distortions such as additive white Gaussian noise, JPEG compression, and blurring. Experimental demonstrate that the proposed scheme is appropriate to identify the forged region by copy-rotate-move forgery. A parallel implementation of 3D Zernike moment analysis. Zernike polynomials are a well known set of functions that find many applications in image or pattern characterization because they allow to construct shape descriptors that are invariant against translations, rotations or scale changes.

The concepts behind them can be extended to higher dimension spaces, making them also fit to describe volumetric data. They have been less used than their properties might suggest due to their high computational cost. We present a parallel implementation of 3D Zernike moments analysis, written in C with CUDA extensions, which makes it practical to employ Zernike descriptors in interactive applications, yielding a performance of several frames per second in voxel datasets about in size. In our contribution, we describe the challenges of implementing 3D Zernike analysis in a general-purpose GPU.

These include how to deal with numerical inaccuracies, due to the high precision demands of the algorithm, or how to deal with the high volume of input data so that it does not become a bottleneck for the system. Improving Zernike moments comparison for optimal similarity and rotation angle retrieval. Zernike moments constitute a powerful shape descriptor in terms of robustness and description capability. However the classical way of comparing two Zernike descriptors only takes into the magnitude of the moments and loses the phase information.

The novelty of our approach is to take advantage of the phase information in the comparison process while still preserving the invariance to rotation. This new Zernike comparator provides a more accurate similarity measure together with the optimal rotation angle between the patterns, while keeping the same complexity as the classical approach. This angle information is particularly of interest for many applications, including 3D scene understanding through images. Experiments demonstrate that our comparator outperforms the classical one in terms of similarity measure.

In particular the robustness of the retrieval against noise and geometric deformation is greatly improved. Moreover, the rotation angle estimation is also more accurate than state-of-the-art algorithms. Combined invariants to similarity transformation and to blur using orthogonal Zernike moments. PubMed Central. The derivation of moment invariants has been extensively investigated in the past decades.

In this paper, we construct a set of invariants derived from Zernike moments which is simultaneously invariant to similarity transformation and to convolution with circularly symmetric point spread function PSF. Two main contributions are provided: the theoretical framework for deriving the Zernike moments of a blurred image and the way to construct the combined geometric-blur invariants. The performance of the proposed descriptors is evaluated with various PSFs and similarity transformations.

The comparison of the proposed method with the existing ones is also provided in terms of pattern recognition accuracy, template matching and robustness to noise. Experimental show that the proposed descriptors perform on the overall better. Range image segmentation using Zernike moment -based generalized edge detector. The authors proposed a novel Zernike moment -based generalized step edge detection method which can be used for segmenting range and intensity images.

A generalized step edge detector is developed to identify different kinds of edges in range images. These edge maps are thinned and linked to provide final segmentation. A generalized edge is modeled in terms of five parameters: orientation, two slopes, one step jump at the location of the edge, and the background gray level.

Two complex and two real Zernike moment -based masks are required to determine all these parameters of the edge model. Theoretical noise analysis is performed to show that these operators are quite noise tolerant. Experimental are included to demonstrate edge-based segmentation technique. Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM. This work is directed toward the development of a computer-aided diagnosis CAD system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant.

Original mammogram is preprocessed to separate the breast region from its background. For training, patches are extracted manually from a preprocessed mammogram. For testing, patches are extracted from a highly dense area identified by clustering technique. For all extracted patches corresponding to a mammogram, Zernike moments of different orders are computed and stored as a feature vector. The experimental study shows that the use of Zernike moments with order 20 and SVM classifier gives better among other studies.

To verify the applicability of Zernike moments as a fitting texture descriptor, the performance of the proposed CAD system is compared with the other well-known texture descriptors namely gray-level co-occurrence matrix GLCM and discrete cosine transform DCT. On soft clipping of Zernike moments for deblurring and enhancement of optical point spread functions.

Blur and noise originating from the physical imaging processes degrade the microscope data. Accurate deblurring techniques require, however, an accurate estimation of the underlying point-spread function PSF. A good representation of PSFs can be achieved by Zernike Polynomials since they offer a compact representation where low-order coefficients represent typical aberrations of optical wavefronts while noise is represented in higher order coefficients. A quantitative description of the noise distribution Gaussian over the Zernike moments of various orders is given which is the basis for the new soft clipping approach for denoising of PSFs.

Instead of discarding moments beyond a certain order, those Zernike moments that are more sensitive to noise are dampened according to the measured distribution and the present noise model. Further, a new scheme to combine experimental and theoretical PSFs in Zernike space is presented. Finally, we demonstrate the advantages of our approach on 3D images of confocal microscopes by generating visually improved volumes. Additionally, we are presenting a method to render the reconstructed using a new volume rendering method that is almost artifact-free. The new approach is based on a Shear-Warp technique, wavelet data encoding techniques and a recent approach to approximate the gray value distribution by a Super spline model.

Image object recognition based on the Zernike moment and neural networks. This paper first give a comprehensive discussion about the concept of artificial neural network its research methods and the relations with information processing. On the basis of such a discussion, we expound the mathematical similarity of artificial neural network and information processing. Then, the paper presents a new method of image recognition based on invariant features and neural network by using image Zernike transform.

The method not only has the invariant properties for rotation, shift and scale of image object, but also has good fault tolerance and robustness. Meanwhile, it is also compared with statistical classifier and invariant moments recognition method. Target recognition of ladar range images using even-order Zernike moments. Ladar range images have attracted considerable attention in automatic target recognition fields.

In this paper, Zernike moments ZMs are applied to classify the target of the range image from an arbitrary azimuth angle. However, ZMs suffer from high computational costs. To improve the performance of target recognition based on small samples, even-order ZMs with serial-parallel backpropagation neural networks BPNNs are applied to recognize the target of the range image. It is found that the rotation invariance and classified performance of the even-order ZMs are both better than for odd-order moments and for moments compressed by principal component analysis.

The experimental demonstrate that combining the even-order ZMs with serial-parallel BPNNs can ificantly improve the recognition rate for small samples. Accurate iris recognition from the distantly acquired face or eye images requires development of effective strategies which can for ificant variations in the segmented iris image quality. Such variations can be highly correlated with the consistency of encoded iris features and the knowledge that such fragile bits can be exploited to improve matching accuracy.

A non-linear approach to simultaneously for both local consistency of iris bit and also the overall quality of the weight map is proposed. Our approach therefore more effectively penalizes the fragile bits while simultaneously rewarding more consistent bits. In order to achieve more stable characterization of local iris features, a Zernike moment -based phase encoding of iris features is proposed. Such Zernike moments -based phase features are computed from the partially overlapping regions to more effectively accommodate local pixel region variations in the normalized iris images.

A t strategy is adopted to simultaneously extract and combine both the global and localized iris features. The superiority of the proposed iris matching strategy is ascertained by providing comparison with several state-of-the-art iris matching algorithms on three publicly available databases: UBIRIS. Our experimental suggest that proposed strategy can achieve ificant improvement in iris matching accuracy over those competing approaches in the literature, i.

A simple approach to quantitative analysis using three-dimensional spectra based on selected Zernike moments. A very simple approach to quantitative analysis is proposed based on the technology of digital image processing using three-dimensional 3D spectra obtained by high-performance liquid chromatography coupled with a diode array detector HPLC-DAD. As the region-based shape features of a grayscale image, Zernike moments with inherently invariance property were employed to establish the linear quantitative models. This approach was applied to the quantitative analysis of three compounds in mixed samples using 3D HPLC-DAD spectra, and three linear models were obtained, respectively.

The correlation coefficients R 2 for training and test sets were more than 0. The analytical suggest that the Zernike moment selected by stepwise regression can be used in the quantitative analysis of target compounds. Our study provides a new idea for quantitative analysis using 3D spectra, which can be extended to the analysis of other 3D spectra obtained by different methods or instruments.

Comparison of organs' shapes with geometric and Zernike 3D moments. The morphological similarity of organs is studied with feature vectors based on geometric and Zernike 3D moments. It is particularly investigated if outliers and average models can be identified. For this purpose, the relative proximity to the mean feature vector is defined, principal coordinate and clustering analyses are also performed.

To study the consistency and usefulness of this approach, 17 livers and 76 hearts voxel models from several sources are considered. In the liver case, models with similar morphological feature are identified. For the limited amount of studied cases, the liver of the ICRP male voxel model is identified as a better surrogate than the female one.

The relative proximity and clustering analysis rather consistently identify outliers and average models. For the two cases, identification of outliers and surrogate of average models is rather robust. However, deeper classification of morphological feature is subject to caution and can only be performed after cross analysis of at least two kinds of feature vectors. Finally, the Zernike moments contain all the information needed to re-construct the studied objects and thus appear as a promising tool to derive statistical organ shapes.

All rights reserved. Efficient 3D geometric and Zernike moments computation from unstructured surface meshes. This paper introduces and evaluates a fast exact algorithm and a series of faster approximate algorithms for the computation of 3D geometric moments from an unstructured surface mesh of triangles. Being based on the object surface reduces the computational complexity of these algorithms with respect to volumetric grid-based algorithms. In contrast, it can only be applied for the computation of geometric moments of homogeneous objects. This advantage and restriction is shared with other proposed algorithms based on the object boundary.

The proposed exact algorithm reduces the computational complexity for computing geometric moments up to order N with respect to ly proposed exact algorithms, from N 9 to N 6. The approximate series algorithm appears as a power series on the rate between triangle size and object size, which can be truncated at any desired degree. The higher the and quality of the triangles, the better the approximation. This approximate algorithm reduces the computational complexity to N 3. In addition, the paper introduces a fast algorithm for the computation of 3D Zernike moments from the computed geometric moments , with a computational complexity N 4 , while the ly proposed algorithm is of order N 6.

The error introduced by the proposed approximate algorithms is evaluated in different shapes and the cost-benefit ratio in terms of error, and computational time is analyzed for different moment orders. This study presents an improved method based on "Gorji et al.

Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of Our method performs better than Gorji's approach and five other state-of-the-art approaches. As a result, only five dof have to be optimized, and the of iteration necessary for registration can be ificantly reduced.

in a phantom study show that an accuracy of approximately 0. Low-order auditory Zernike moment : a novel approach for robust music identification in the compressed domain. Audio identification via fingerprint has been an active research field for years. It will be interesting if a compressed unknown audio fragment could be directly recognized from the database without decompressing it into the wave format at first.

So far, very few algorithms run directly on the compressed domain for music information retrieval, and most of them take advantage of the modified discrete cosine transform coefficients or derived cepstrum and energy type of features. As a first attempt, we propose in this paper utilizing compressed domain auditory Zernike moment adapted from image processing techniques as the key feature to devise a novel robust audio identification algorithm.

Such fingerprint exhibits strong robustness, due to its statistically stable nature, against various audio al distortions such as recompression, noise contamination, echo adding, equalization, band-pass filtering, pitch shifting, and slight time scale modification.

Chiral extrapolation of the leading hadronic contribution to the muon anomalous magnetic moment. A lattice computation of the leading-order hadronic contribution to the muon anomalous magnetic moment can potentially help reduce the error on the Standard Model prediction for this quantity, if sufficient control of all systematic errors affecting such a computation can be achieved. One of these systematic errors is that associated with the extrapolation to the physical pion mass from values on the lattice larger than the physical pion mass.

This remains true even if various tricks to improve the reliability of the chiral extrapolation employed in the literature are taken into . In addition, while chiral perturbation theory also predicts the dependence on the pion mass of the leading-order hadronic contribution to the muon anomalous magnetic moment as the chiral limit is approached, this prediction turns out to be of no practical use because the physical pion mass is larger than the muon mass that sets the scale for the onset of this behavior.

The application of 3D Zernike moments for the description of "model-free" molecular structure, functional motion, and structural reliability. Protein structures are not static entities consisting of equally well-determined atomic coordinates.

Horny fem Le Fauga

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