Moment Invariants Research Articles

Identification of an Individual's Iris Using Euclidean and Mahalanobis Diagrams

The purpose of this study is to compare Euclidean and Mahalanobis geometry as a means of identifying a person using their iris. Iris is the only biometric that is truly unique and is extremely difficult to perform, making it the single most important consideration in the improvement of system security. To obtain the desired results, namely preprocessing and feature extraction, various methods will be used in image processing. Methods like the Gaussian filter, the operator sobel, and thresholding will be used in the pengolahan. Utilize the United Moment Invariant method to extend the circle (UMI). For projects that use the method of comparing the strengths of FAR and GAR, a smaller FAR was obtained for the eulidean to mahalanobis ratio. Additionally, value distance mahalanobis is smaller compared to FAR for GAR penetration. Keywords: Gaussian Filter, Iris, Sobel Operator, United Moment Invariat

Classification of IR Images of Small Eruptions at the Erebus Volcano, Antarctica, With Zernike Moments and Support Vector Machine

AbstractTo investigate the explosive nature of the Erebus volcano, Antarctica, millions of infrared images of the Ray lava lake were analyzed to identify time and position of non‐explosive small eruptions (NESEs). We developed a new technique based on two steps: (a) feature recognition using Zernike moments of the IR images and (b) classification using Support Vector Machine (SVM) applied to Zernike moments. Measures of the performance score were at least higher than 97% via different metrics for different categorization tasks. We observed distinctly different Zernike spectrums between NESEs and no‐eruption images. In the three months with the best quality images (December 2013, December 2014, and January 2015), out of about one million images per month, 654, 405, and 3,650 NESEs were detected, respectively. Using k‐means clustering three activity regimes emerged: low (≤4 NESEs per hour), intermediate (4 <NESEs per hour <14), and intense (≥14 NESEs per hour). December 2013 and December 2014 were associated with low and medium activity regimes only, while January 2015 had mainly medium and intense activity. The frequency of large eruptions and of NESEs do not show a correlation, supporting the hypothesis of the existence of multiple deep processes at the origin of the gas release. Space analysis of NESEs shows that they emerge over the surface primarily above the conduit, but not isotropically, pointing to complex interaction with the magma lake convection.

CNN-Based Laue Spot Morphology Predictor for Reliable Crystallographic Descriptor Estimation.

Laue microdiffraction is an X-ray diffraction technique that allows for the non-destructive acquisition of spatial maps of crystallographic orientation and the strain state of (poly)crystalline specimens. To do so, diffraction patterns, consisting of thousands of Laue spots, are collected and analyzed at each location of the spatial maps. Each spot of these so-called Laue patterns has to be accurately characterized with respect to its position, size and shape for subsequent analyses including indexing and strain analysis. In the present paper, several approaches for estimating these descriptors that have been proposed in the literature, such as methods based on image moments or function fitting, are reviewed. However, with the increasing size and quantity of Laue image data measured at synchrotron sources, some datasets become unfeasible in terms of computational requirements. Moreover, for irregular Laue spots resulting, e.g., from overlaps and extended crystal defects, the exact shape and, more importantly, the position are ill-defined. To tackle these shortcomings, a procedure using convolutional neural networks is presented, allowing for a significant acceleration of the characterization of Laue spots, while simultaneously estimating the quality of a Laue spot for further analyses. When tested on unseen Laue spots, this approach led to an acceleration of 77 times using a GPU while maintaining high levels of accuracy.

Proposal and Implementation of a Procedure for Compliance Recognition of Objects with Smart Tactile Sensors.

This paper presents a procedure for classifying objects based on their compliance with information gathered using tactile sensors. Specifically, smart tactile sensors provide the raw moments of the tactile image when the object is squeezed and desqueezed. A set of simple parameters from moment-versus-time graphs are proposed as features, to build the input vector of a classifier. The extraction of these features was implemented in the field programmable gate array (FPGA) of a system on chip (SoC), while the classifier was implemented in its ARM core. Many different options were realized and analyzed, depending on their complexity and performance in terms of resource usage and accuracy of classification. A classification accuracy of over 94% was achieved for a set of 42 different classes. The proposed approach is intended for developing architectures with preprocessing on the embedded FPGA of smart tactile sensors, to obtain high performance in real-time complex robotic systems.

Moment-based space-variant Shack–Hartmann wavefront reconstruction

This article presents an approach for space-variant Shack–Hartmann wavefront reconstruction based on image moment theory. We derive the relation between the moment of a pair of subimages and the local transformation coefficients. A special solution is obtained from this relation using the square guide ‘star’. Our moment-based wavefront reconstruction has reduced computational complexity compared to the iteration-based algorithm. We execute image restorations using both the tiling strategy with 5 × 5 PSFs and the conventional strategy with a global average PSF. Our approach is supported by visual and quantitative evaluations.

A robust semi-fragile watermarking system using Pseudo-Zernike moments and dual tree complex wavelet transform for social media content authentication.

With the growing use of mobile devices and Online Social Networks (OSNs), sharing digital content, especially digital images is extremely high as well as popular. This made us convenient to handle the ongoing COVID-19 crisis which has brought about years of change in the sharing of digital content online. On the other hand, the digital image processing tools which are powerful enough to make the perfect image duplication compromises the privacy of the transmitted digital content. Therefore, content authentication, proof of ownership, and integrity of digital images are considered crucial problems in the world of digital that can be accomplished by employing a digital watermarking technique. On contrary, watermarking issues are to triumph trade-offs among imperceptibility, robustness, and payload. However, most existing systems are unable to handle the problem of tamper detection and recovery in case of intentional and unintentional attacks concerning these trade-offs. Also, the existing system fails to withstand the geometrical attacks. To resolve the above shortcomings, this proposed work offers a new multi-biometric based semi-fragile watermarking system using Dual-Tree Complex Wavelet Transform (DTCWT) and pseudo-Zernike moments (PZM) for content authentication of social media data. In this research work, the DTCWT-based coefficients are used for achieving maximum embedding capacity. The Rotation and noise invariance properties of Pseudo Zernike moments make the system attain the highest level of robustness when compared to conventional watermarking systems. To achieve authentication and proof of identity, the watermarks of about four numbers are used for embedding as a replacement for a single watermark image in traditional systems. Among four watermarks, three are the biometric images namely Logo or unique image of the user, fingerprint biometric of the owner, and the metadata of the original media to be transmitted. In addition, to achieve the tamper localization property, the Pseudo Zernike moments of the original cover image are obtained as a feature vector and also embedded as a watermark. To attain a better level of security, each watermark is converted into Zernike moments, Arnold scrambled image, and SHA outputs respectively. Then, to sustain the trade-off among the watermarking parameters, the optimal embedding location is determined. Moreover, the watermarked image is also signed by the owner's other biometric namely digital signature, and converted into Public key matrix Pkm and embedded onto the higher frequency subband namely, HL of the 1-level DWT. The proposed system also accomplishes a multi-level authentication, among that the first level is attained by the decryption of the extracted multiple watermark images with the help of the appropriate decryption mechanism which is followed by the comparison of the authentication key which is extracted using the key which is regenerated at the receiver's end. The simulation outcomes evident that the proposed system shows superior performance towards content authentication, to most remarkable intentional and unintentional attacks among the existing watermarking systems.

Vision-Based Impedance Control of an Aerial Manipulator Using a Nonlinear Observer

Contact-based aerial interaction control in unknown environments is a challenging issue. To solve this problem, this paper presents an image-based impedance control scheme for an aerial manipulator. Unlike points and image moments, the line features considered in this paper cannot uniquely determine the pose of the onboard camera. Therefore, a nonlinear observer is proposed to online provide supplementary 3D information of the system. After that, a hierarchical tracking controller, which is equipped with an image-space impedance filter, is designed to implement a trajectory tracking task with tunable compliance, while at the same time achieving a subtask that aims to determine the final pose of the aerial manipulator. By planning task-space trajectories, desired interaction behavior can be specified without relying on the global position of the system. Furthermore, the effectiveness criterion of the observer is derived, and the stability of the system is proven. Several experiments, including a push-and-slide task, robustness analysis, and a comparison with the state of the art have been conducted to demonstrate the feasibility of the method. Note to Practitioners—This article presents a method for controlling an aerial manipulator to interact with unknown environments without measuring the global position of both the environment and the aerial manipulator. Furthermore, unlike the existing methods, the proposed visual servoing method is based on line features, which are generally more robust than point features. Moreover, it would be intuitive for users to specify the approaching path and the interaction behavior of the aerial manipulator by just planning the image feature trajectories and the desired distance between the aerial manipulator and the environment. This distance is online estimated using the visual feedback of a monocular camera, instead of relying on any costly 3D-vision sensors or lidars, making it an economical solution for aerial manipulation in field environments.

Development of Neural Network Recognition Model for Optical Illusion Images

In the battlefield, tank armored vehicles have high mobility, strong defense and stable transportation capabilities, and play a role that cannot be ignored. However, its appearance and camouflage pattern design often have the camouflage effect of geometric optical illusion. The good recognition effect can ensure the safety of soldiers, and also avoid the loss of life and property of the people by accidentally hitting the surrounding houses. Therefore, how to quickly and effectively identify its appearance has become an urgent problem to be solved in the military. In this paper, the outline of the vehicle is disassembled into 12 most representative basic shapes for analysis, using image preprocessing technology, invariant moment feature extraction, and developing a BPNN(back propagation neural network) recognition model, which effectively improves the recognition rate of tank armored vehicles over above 95%.

Robust Image-Based Landing Control of a Quadrotor on an Unpredictable Moving Vehicle Using Circle Features

This paper addresses the landing problem of a quadrotor on an unpredictable moving vehicle, using a robust image-based visual servoing (IBVS) method. The circle-based image moments are defined to construct image dynamics, and the passivity-like property of the circle features is preserved by reprojecting to the virtual image plane. The landing control system is decoupled into translation and rotation modules due to the rotation invariance of the proposed circle features. First, by exploiting the error transformation in the image space, a robust IBVS controller can overcome the lack of both the desired depth information of target features and the velocity feedback of the target. Next, an adaptive geometric attitude controller is developed directly using rotation matrices to avoid the singularities of Euler-angles and the ambiguity of quaternions. One benefit of the proposed scheme is that it can potentially improve the camera visibility, guarantee the transient and steady-state behaviors in image space, and be efficiently implemented on the low-cost quadrotor. Finally, The stability analysis is presented using Lyapunov stability theory on cascaded systems, and the effectiveness of the proposed control strategy is demonstrated through simulations and experiments. Note to Practitioners—The motivation of this paper is to investigate a practical control strategy for the image-based landing control of underactuated quadrotors on an unpredictable moving vehicle. In most of the existing image-based landing control schemes for underactuated quadrotors, having the prior predictive model of the moving landing vehicle to provide a feed-forward compensation during the landing maneuver. However, due to the fact that the landing environment and vehicle are primarily stochastic, resulting in no predictive models are valid in practice. Therefore, this paper suggests a robust image-based landing control strategy without the model or state of the moving landing vehicle. In particular, a novel virtual circle feature, possessing the characteristic of rotation invariance, is designed for the landing of underactuated quadrotors, which decouples the landing system and simplifies the control design. Moreover, the image feature errors are directly retained within prescribed performance funnels in the image space. As a result, the transient and steady-state landing behaviors can be implicitly guaranteed in Cartesian space. The stability and convergence of the system are analyzed mathematically and the experiment using quadrotors provides promising results. In ongoing research, we are addressing the issues of collision avoidances and unknown disturbances to provide a more realistic setup for the autonomous deployment and recovery of underactuated quadrotors in GPS-denied environments.

Transverse mixing zone under dispersion in porous media: Effects of medium heterogeneity and fluid rheology

The addition of an environmental remediation modifier—polymer solution—to a Newtonian fluid expands the distribution of remediation agents injected in situ into saturated aquifers (affecting plume velocity and deformation), enhancing remediation efficiency. However, the effect of the flow properties of the polymer solution on the macroscopic transverse dispersion remains poorly understood. In this work, a transparent thin-layer two-dimensional sandbox was constructed to simulate the aquifer, and the transverse distribution range of colored solute—permanganate solution and viscous shear-thinning fluid (permanganate solution + xanthan gum)—was captured in real-time by a camera device during transport in porous media. The boundary dispersion coefficient was obtained by fitting a breakthrough curve of the boundary concentration, while the overall plume dispersion coefficient was determined via image moment analysis. The effects of fluid rheology and heterogeneity on the transverse mixing of the plume were analyzed, and the mechanism of viscoelasticity-induced transverse dispersion and mixing enhancement was summarized. The results indicated that the anisotropic stress generated by polymer fluid deformation at high water velocity increased the fluctuation and transverse distribution of the plume, while higher-viscosity polymers increased the initial extrusion swelling and additional compressive stress, covering a larger area. Xanthan gum enhanced the transverse distribution of the plume mainly through initial injection-extrusion expansion effect, viscoelastic stability of the post-injection part, and streamline crossing attributed to elastic turbulence. This study also verified that the shear-thinning fluid enhanced the effect of transverse dispersion and mixing under heterogeneous conditions, providing insights applicable to groundwater remediation.

Search for identical points in the inter-pixel space of video images

One of the ways to describe objects in images is to identify some of their characteristic points or points of attention. Areas surrounding attention points are described by descriptors (a set of features) in such a way that they can be identified and compared. On these features the search for identical points on other images is carried out by scanning them with a sliding window. The most famous descriptors and methods for finding identical points are: SIFT, SURF, GLOH, BRIEF and others. This group of methods is characterized by the fact that the displacement of identical points in video images can be arbitrary, but the accuracy of calculating their coordinates depends on the bit grid of video images and, in the best case, is equal to the interpixel distance. Another group of methods that can be used to track identical points of video images are methods built on the basis of optical flow calculation. One of the popular methods of tracking points based on optical flow calculation is the Lucas-Kanade method. It allows you to calculate the displacement of points in the interpixel space due to the solution of differential equations. To date, the Lucas-Kanade method has several modifications. A limitation of these methods is that the neighborhoods of the shifted points must overlap to a large extent. The article investigates and proposes the complex application of methods of scanning video images with a sliding window and differential calculation of optical flow, which allows to increase the accuracy and speed of calculating the coordinates of identical points in the images in relation to the search for these points only by scanning. A more accurate calculation of the coordinates of the characteristic points of the object in the interpixel space of video images will lead to a more accurate determination of the position and orientation of these objects in 3D space. The simulation was carried out using the method of rough search for identical points of video images described by invariant moments and specifying their coordinates using the Lucas-Kanade point tracking method. The simulation results indicate an increase in speed by almost an order of magnitude and, according to indirect estimates, the accuracy of calculations.

A Novel Denoising Algorithm Based on Wavelet and Non-Local Moment Mean Filtering

Denoising is the basis and premise of image processing and an important part of image preprocessing. Denoising can effectively improve image quality, which contributes to subsequent image processing such as image segmentation, feature extraction, and so on. In this paper, we propose a novel image denoising method based on wavelet transform and nonlocal moment mean filtering approach (NMM). The noisy image is firstly denoised by a wavelet-based soft-thresholding denoising technique and NMM is then utilized to further eliminate the rest noises. Meanwhile, the fusion of moment invariants increases the robustness of our denoising algorithm due to the invariance of image scaling, translation, and rotation of color moments. Experiments show that our algorithm achieves a better denoising effect compared with some other denoising approaches.

On The Potential of Image Moments for Medical Diagnosis.

Medical imaging is widely used for diagnosis and postoperative or post-therapy monitoring. The ever-increasing number of images produced has encouraged the introduction of automated methods to assist doctors or pathologists. In recent years, especially after the advent of convolutional neural networks, many researchers have focused on this approach, considering it to be the only method for diagnosis since it can perform a direct classification of images. However, many diagnostic systems still rely on handcrafted features to improve interpretability and limit resource consumption. In this work, we focused our efforts on orthogonal moments, first by providing an overview and taxonomy of their macrocategories and then by analysing their classification performance on very different medical tasks represented by four public benchmark data sets. The results confirmed that convolutional neural networks achieved excellent performance on all tasks. Despite being composed of much fewer features than those extracted by the networks, orthogonal moments proved to be competitive with them, showing comparable and, in some cases, better performance. In addition, Cartesian and harmonic categories provided a very low standard deviation, proving their robustness in medical diagnostic tasks. We strongly believe that the integration of the studied orthogonal moments can lead to more robust and reliable diagnostic systems, considering the performance obtained and the low variation of the results. Finally, since they have been shown to be effective on both magnetic resonance and computed tomography images, they can be easily extended to other imaging techniques.

Robust Image Watermarking With Quaternion Fractional-Order Polar Harmonic-Fourier Moments Based On Wavelet Transformation: Resistance Against Rotation Attacks

This study presents a zero-watermarking algorithm that can resist rotation attacks. The algorithm uses quaternion fractional-order polar harmonic-Fourier moments (QFr-PHFMs) based on wavelet-transformation. First, the wavelet-transformation is applied to each component of the host image, which is in RGB three-channel color. The low-frequency sub-bands of each component are then extracted and represented using quaternion algebra. Multiple QFr-PHFMs are calculated, and the invariants of the QFr-PHFMs are utilized to establish the watermark system. The watermark extraction process is also simplified. The detection of the image requires a two-level wavelet transformation, followed by the calculation of multiple invariant moments of the low-frequency sub-image. The experimental results are shown and compared with similar methods. Simulations show that this method can produce high-quality visual effects and withstand noise, filtering, JPEG compression, and cropping attacks.

Sedenion polar harmonic Fourier moments and their application in multi-view color image watermarking

In recent years, the research on image moments has made significant progress, transitioning from gray image moments to color image moments and from gray stereo image moments to color stereo image moments. However, the application of stereo image moments is limited to binocular stereo images with two views, and cannot handle images with more than two views. Multi-view images are more promising but are difficult to handle due to their intricate structure. Based on the sedenion theory and polar harmonic Fourier moments (PHFMs), this paper proposes the sedenion polar harmonic Fourier moments (SPHFMs) for processing multi-view color images. The imaginary part of sedenion encodes all views of multi-view color images, thus allowing all views to be processed as a single unit and internal correlations between all views to be maintained. The results of the stability experiment and image reconstruction experiment demonstrate that good stability and high image reconstruction performance are achieved when the SPHFMs are applied to processing multi-view color images. Since existing multi-view color image watermarking methods are not resistant to geometric attacks, we use SPHFMs for multi-view color image watermarking, which exhibits good imperceptibility and robustness.

Development of welding discontinuity identification system using statistical texture feature extraction and ANN classification on digital radiographic image

Discontinuity in welds is one of the causes of the quality of a connection in the material decreases function. Undamaged test with radiographic method is one of the tests to see the quality of a weld. The test results are radiograph images and evaluated by a radiographer. So this research is designed by optimizing a system to help the work of a radiography expert in identifying discontinuities by utilizing the Matlab Application. On this system uses the method of characteristic extraction and classification of neural networks (AAN). The system uses a characteristic extraction method with geometric invariant moment (GIM) algorithms and a gray level co-occurenece matrix (GLCM) as identification values used in the classification process. The calcification process uses a backpropagation-type multilayer Artificial Neural Network. The types of discontinuities used as data in this system are incompleted of penetration, crack, wormhole, and distributed porosity using a total of 800 datasets of radiograph imagery data. This data sharing is organized using k fold cross validation. The study conducted 15 experiments in system testing to prove the truth in identifying. The results of the experiment resulted in the highest average performance score reaching 93.33%

Typical Fault Modeling and Vibration Characteristics of the Turbocharger Rotor System

To study the typical failure mechanism (rotor unbalance, rotor friction, and rotor crack) and vibration characteristics of the turbocharger rotor system, a rotor system dynamics simulation model was established by an improved four-node aggregate parameter method. The geometric and physical characteristics of the rotor system under three failure states and its dynamics under operation were analyzed. Thus, a typical failure dynamics simulation model of the rotor system was established. On this basis, the output failure simulation signal was extracted using the Hu invariant moment feature extraction method to analyze the system vibration characteristics under each typical failure state of the rotor system. The results show that the model in this paper can effectively reduce the computational volume and computational time, and the errors of numerical simulation were less than 3%. When an unbalance fault occurred in the rotor system, the shaft trajectory was “0” shaped and the response spectrum was dominated by 1X. When the rotor system was frictional, the shaft trajectory was a slightly concave “8” shape, and the response spectrum was dominated by 0.5X. When the rotor system was cracked, the axial trajectory was a “vortex”, and the response spectrum was dominated by 0.5X. Thus, the study of typical failure mechanism and vibration characteristics of a turbocharger rotor system by simulation calculation is effective and has good research prospects, providing an important technical reference for dynamic analysis and fault diagnosis of the rotor system.

Quaternion fractional-order weighted generalized Laguerre–Fourier moments and moment invariants for color image analysis

Orthogonal moments based on Laguerre polynomials, especially the quaternion representation of fractional-order orthogonal moments have attracted interest in the imaging field. However, most of the existing quaternion fractional-order color orthogonal moments (QFr-COMs) based on Laguerre polynomials are constructed on Cartesian coordinate space and do not have direct rotation geometric invariance. Inspired by the idea of polynomial transformation, we present a weighted radial normalized fractional-order generalized Laguerre polynomials (WRNFr-GLPs) in polar coordinate space. On the basis, a new set of quaternion fractional-order weighted generalized Laguerre–Fourier moments (QFr-WGLFMs) is proposed and a fast and reliable computing method for geometric invariance of QFr-WGLFMs also is introduced in this paper. Theoretical analyses and experimental results showed that the proposed QFr-WGLFMs and moment invariants offer enhanced image reconstruction and pattern recognition compared with the existing quaternion fractional-order-based orthogonal moments and deep learning-based​ methods, especially under the conditions of image processing with noise and smooth filtering.

New physics contributions to moments of inclusive b → c semileptonic decays

Inclusive semileptonic B → {X}_cell {overline{nu}}_{ell } decays, where ℓ = μ, e, are by now standard candles in the determination of the CKM element |Vcb|. These determinations rely on the heavy-quark expansion and use moments of decay spectra to extract the non-perturbative parameters directly from data under the standard model assumption.At the same time, new physics could influence the moments of the inclusive decay. In this paper, we compute power-corrections and next-to-leading order corrections in the strong coupling constant using the full basis of dimension-six new physics operators for the inclusive B → {X}_cell overline{nu} decay. We provide predictions for lepton energy, hadronic and leptonic invariant mass moments, and perform a phenomenological study to show the possible impact of new physics. Our results could be used to perform a global fit including new physics contributions.

Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques.

Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI). The experiments were carried out on a database obtained from the Vue PACS-Carestream software, which contained 132 images of ATFL and normal (healthy) ankles. Because there were only a few images, image augmentation techniques was used to increase the number of images in the database. Following that, various feature extraction algorithms (GLCM, LBP, and HU invariant moments) and classifiers such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were used. Based on the results from this analysis, for cases that lack clear morphologies, the method delivers a hit rate of 85.03% with an increase of 22% over the human expert-based analysis.