Normalized Cross Correlation Research Articles

Spatially Modulated Fiber Speckle for High-Sensitivity Refractive Index Sensing.

A fiber speckle sensor (FSS) based on a tapered multimode fiber (TMMF) has been developed to measure liquid analyte refractive index (RI) in this work. By the lateral and axial offset of input light into TMMF, several high-order modes are excited in TMMF, and the speckle pattern is spatially modulated, which affects an asymmetrical speckle pattern with a random intensity distribution at the output of TMMF. When the TMMF is immersed in the liquid analyte with RI variation, it influences the guided modes, as well as the mode interference, in TMMF. A digital image correlations method with zero-mean normalized cross-correlation coefficient is explored to digitize the speckle image differences, analyzing the RI variation. It is found that the lateral- and axial-offsets-induced speckle sensor can enhance the RI sensitivity from 6.41 to 19.52 RIU-1 compared to the one without offset. The developed TMMF speckle sensor shows an RI resolution of 5.84 × 10-5 over a linear response range of 1.3164 to 1.3588 at 1550 nm. The experimental results indicate the FSS provides a simple, efficient, and economic approach to RI sensing, which exhibits an enormous potential in the image-based ocean-sensing application.

GAN-LSTM-Based ADS-B Attack Detection in the Context of Air Traffic Control

Automatic Dependent Surveillance-Broadcasting (ADS-B) is a surveillance technology strongly promoted by the International Civil Aviation Organization. It has been widely used in commercial and general aviation, to provide support for the normal operation of air traffic control (ATC) in commercial aviation. However, the openness of the ADS-B protocol makes it extremely vulnerable to cyber attacks. Previous research did not specifically consider the application scenarios of ATC in commercial air transport, and there is a problem of low attack detection rates. This paper focuses on ADS-B attack detection under the background of ATC. We combine the flight plan with ADS-B information to construct an airspace flight image stream and process the image stream using a generative adversarial network-long short-term memory (GAN-LSTM) model to predict future images. Then, we identify abnormal images based on the normalized cross correlation and mark anomalous targets. Our method can quickly locate anomalous targets in the controlled airspace. Based on real flight data, abnormal data for various malicious attacks were forged. We evaluated the detection performance of the method through a confusion matrix and several performance indices. The final experimental results showed that our detection scheme exhibited good detection performances for various attacks, with an average detection accuracy of 92.3%, a false positive rate of 11.9%, and a false negative rate of 6.2%. This approach guarantees the information security of ADS-B, thereby improving the operational security of ATC.

Correlation of Ground Deformation Induced by the 6 February 2023 M7.8 and M7.5 Earthquakes in Turkey Inferred by Sentinel-2 and Critical Exposure in Gaziantep and Kahramanmaraş Cities

On 6 February 2023, an M7.8 devastating earthquake started rupturing the East Anatolian fault system in Turkey, resulting in intense shaking that lasted over a minute. A second earthquake of magnitude 7.5 struck near the city of Elbistan a few hours later. Both of these events are associated with the East Anatolian fault system. The earthquake sequence caused widespread damage and collapse of structures in densely populated areas throughout the Southern Turkey and Northern Syria regions and a very large number of human losses. This study focuses on the correlation of the ground deformation with the critical exposure of the infrastructures of Gaziantep and Kahramanmaraş cities. The estimation of the ground deformation of the affected area is achieved with the use of Copernicus Sentinel-2 products and the Normalized Cross Correlation algorithm (NCC) of image matching. The results of the East–West component show that specific sections of the region moved towards the East direction, reaching displacement measurements of 5.4 m, while other sections moved towards the West direction, reaching displacement measurements of 2.8 m. The results of the North–South component show that almost the whole affected area moved towards the North direction, with specific areas reaching displacements of 5.5 m, and a few exemptions, as some areas moved towards the South direction, with displacements reaching even 6.9 m. Regarding the cities of Kahramanmaraş and Gaziantep, their estimated movement direction is North-West and North-East, respectively, and is consistent with the movements of the Arabian and Anatolian Plates in which they are located. Important infrastructures of the study areas (education, museums, libraries, hospitals, monuments, airports, roads and railways) are superimposed on the findings, enabling us to detect the critical exposure rapidly.

Real-Time 4-D-Cone Beam CT Accurate Estimation Based on Single-Angle Projection via Dual-Attention Mechanism Residual Network

Recently rapid and efficient localization of lung tumors based on single cone beam CT (CBCT) projection has attracted great interest in the radiation oncology community. High quality 4D-CBCT benefits accurate monitoring of moving targets during radiation therapy (RT). However, conventional iterative 4D-CBCT reconstruction process is heavily time-consuming. To address these issues, a motion sensitive cascade model was proposed. The proposed cascade model is composed of dual attention mechanism together with residual network (DA-ResNet) and a principal component analysis (PCA) model. It maps single projection from different breathing phase to each phase 3D-CBCT for achieving real time 4D-CBCT. The dual attention mechanism focuses on the motion information of both low-level features and high-level features to improve accuracy and efficiency of the network. The PCA model ensures a real time motion representation scheme. Compared with state-of-the-art networks, the proposed method outperformed them in the quantification labels of mean absolute error (MAE), R-squared (R2), normalized cross correlation (NCC), and structural similarity index measure (SSIM). This experiment was verified both on simulation and clinical data to support the conclusion.

Co-Design Dedicated System for Efficient Object Tracking Using Swarm Intelligence-Oriented Search Strategies

The template matching technique is one of the most applied methods to find patterns in images, in which a reduced-size image, called a target, is searched within another image that represents the overall environment. In this work, template matching is used via a co-design system. A hardware coprocessor is designed for the computationally demanding step of template matching, which is the calculation of the normalized cross-correlation coefficient. This computation allows invariance in the global brightness changes in the images, but it is computationally more expensive when using images of larger dimensions, or even sets of images. Furthermore, we investigate the performance of six different swarm intelligence techniques aiming to accelerate the target search process. To evaluate the proposed design, the processing time, the number of iterations, and the success rate were compared. The results show that it is possible to obtain approaches capable of processing video images at 30 frames per second with an acceptable average success rate for detecting the tracked target. The search strategies based on PSO, ABC, FFA, and CS are able to meet the processing time of 30 frame/s, yielding average accuracy rates above 80% for the pipelined co-design implementation. However, FWA, EHO, and BFOA could not achieve the required timing restriction, and they achieved an acceptance rate around 60%. Among all the investigated search strategies, the PSO provides the best performance, yielding an average processing time of 16.22 ms coupled with a 95% success rate.

Low complexity adaptive ultrasound image beamformer combined with improved multiphase apodization with cross-correlation

In this paper, an ultrasound imaging method combined with low-complexity adaptive beamformer (LCA) and improved multiphase apodization with cross-correlation (IMPAX) is proposed to improve image resolution and contrast with low hardware cost. Firstly, the delayed echo signal is apodized by the LCA to obtain a narrow mainlobe width echo signal and LCA output. Then, multiple pairs of complementary square-wave phase apodizations are applied to the apodized echo signal to obtain corresponding signal pairs, which are used to calculate the normalized cross-correlation (NCC) matrix. Finally, the average value of the NCC matrices is filtered by 2-D means, and the filtered result is introduced as the weighting factor for the LCA output. The simulation and experimental results show that the proposed LCA-IMPAX can effectively reduce the mainlobe width, suppress clutter, and be robust to noise. Compared with DAS, LCA, and MPAX, for simulated point targets, the full-width at half-maximum (FWHM, −6dB) of LCA-IMPAX is reduced by 49.22%, 10.06%, and 48.67%, respectively. For simulated cyst, the CR is improved by 219.91%, 138.08%, and 103.44%, respectively. For experimental cysts, the CR is improved by an average of 145.00%, 136.14%, and 55.09%, respectively. The results of human heart data indicate that LCA-IMPAX has good imaging quality in vivo. Since the proposed method does not involve covariance matrix inversion, it can be applied in real-time imaging systems.

Detecting Recent Dynamics in Large-Scale Landslides via the Digital Image Correlation of Airborne Optic and LiDAR Datasets: Test Sites in South Tyrol (Italy)

Large-scale slow-moving deep-seated landslides are complex and potentially highly damaging phenomena. The detection of their dynamics in terms of displacement rate distribution is therefore a key point to achieve a better understanding of their behavior and support risk management. Due to their large dimensions, ranging from 1.5 to almost 4 km2, in situ monitoring is generally integrated using satellite and airborne remote sensing techniques. In the framework of the EFRE-FESR SoLoMon project, three test-sites located in the Autonomous Province of Bolzano (Italy) were selected for testing the possibility of retrieving significant slope displacement data from the analysis of multi-temporal airborne optic and light detection and ranging (LiDAR) surveys with digital image correlation (DIC) algorithms such as normalized cross-correlation (NCC) and phase correlation (PC). The test-sites were selected for a number of reasons: they are relevant in terms of hazard and risk; they are representative of different type of slope movements (earth-slides, deep seated gravitational slope Deformation and rockslides), and different rates of displacement (from few cm/years to some m/years); and they have been mapped and monitored with ground-based systems for many years (DIC results can be validated both qualitatively and quantitatively). Specifically, NCC and PC algorithms were applied to high-resolution (5 to 25 cm/px) airborne optic and LiDAR-derived datasets (such as hillshade and slope maps computed from digital terrain models) acquired during the 2019–2021 period. Qualitative and quantitative validation was performed based on periodic GNSS surveys as well as on manual homologous point tracking. The displacement maps highlight that both DIC algorithms succeed in identifying and quantifying slope movements of multi-pixel magnitude in non-densely vegetated areas, while they struggle to quantify displacement patterns in areas characterized by movements of sub-pixel magnitude, especially if densely vegetated. Nonetheless, in all three landslides, they proved to be able to differentiate stable and active parts at the slope scale, thus representing a useful integration of punctual ground-based monitoring systems.

Face, Fingerprint, and Signature based Multimodal Biometric System using Score Level and Decision Level Fusion Approaches

Principal Component Analysis (PCA) is the best face recognition method. This research suggests PCA for fingerprint and signature recognition. Simple image processing transforms like DCT, 2D-DCT, DWT, SWT, 2D-SWT, SVD (Singular Vector Decomposition), Entropy, and Rank can be used for feature extraction. These transforms and measures are utilized with PCA as a feature extraction module to construct uni-modal and multimodal biometric systems using face, fingerprint, and signature modalities. Most PCA biometrics systems compare stored template to claimed identification using Euclidean distance. This paper proposes matching modules using similarity and dissimilarity measures viz. Absolute Pearson's Correlation Coefficient (APCC), Absolute Uncentered Pearson's Correlation Coefficient (AUPCC), Bray Curtis Distance (BC), Canberra distance (CB), Chebyshev Distance (CBS), Chessboard Distance (CSB), City block or Manhattan distance (CTB), Cross Correlation (CC), Dot product (DP), Euclidean distance (EUC), Extended Jaccard Distance (EJ), Hamming Distance (HM), Harmonically Summed Euclidean distance (HSEUC), Kendall Correlation Coefficient (KCC), Mahalanobis Distance (MH), Minimum Coordinate Difference (MCD), Minkowiski distance (MNK), Multivariate Kurtosis Coefficient (MVK), Multivariate Skew (MVS), Normalized City Block or Manhattan distance (NCTB), Normalized Cross-correlation (NCC), Normalized Euclidean distance (NEUC), Pearson’s Cosine Distance (PCOS), Pearson's Correlation Coefficient (PCC), Pearson's Absolute Value Dissimilarity (PAVD), Pearson's Linear Dissimilarity (PLDISS), Spearman Correlation Coefficient (SCC), Standardized Euclidean Distance (SEUC), Uncentered Pearson's Correlation Coefficient (UPCC), Wave-Hedges Distance (WVH). This study again discusses score level fusion of face, fingerprint, and signature using sum and max rules, z-score normalization, and decision level fusion using AND rule.

An effective electrocardiogram segments denoising method combined with ensemble empirical mode decomposition, empirical mode decomposition, and wavelet packet

AbstractElectrocardiogram (ECG) is the most extensively applied diagnostic approach for heart diseases. However, an ECG signal is a weak bioelectrical signal and is easily disturbed by baseline wander, powerline interference, and muscle artefacts, which make detection of heart diseases more difficult. Therefore, it is very important to denoise the contaminated ECG signal in practical application. In this article, an effective ECG segments denoising method combining the ensemble empirical mode decomposition (EEMD), empirical mode decomposition (EMD), and wavelet packet (WP) is designed. The ECG signal is decomposed using the EEMD for the first time, and then the highest frequency component is decomposed by the EMD for the second time, and the high frequency components obtained from the second time are decomposed and reconstructed by the WP for the third time. Finally, the processed signal components are fused to obtain the denoised ECG signal. Furthermore, the signal‐to‐noise ratio (SNR), mean square error (MSE), root mean square error (RMSE), and normalised cross correlation coefficient (R) are used to evaluate the noise reduction algorithm. The mean SNR, MSE, RMSE, and R are 5.7427, 0.0071, 0.0551, and 0.9050 in the China Physiological Signal Challenge 2018 dataset. Compared with others denoising methods, the experimental results not only exhibit that the SNR of the ECG signal is effectively improved, but also show that the details of the ECG signal are fully retained, laying a solid foundation for the automatic detection of ECG segments.

A Fault Diagnosis Method for Rotating Machinery With Semi-Supervised Graph Convolutional Network and Images Converted From Vibration Signals

In recent years, intelligent fault diagnosis has been widely investigated. Deep learning fault diagnosis methods for rotating machinery are primarily based on the assumption that labeled data are abundant. However, it is difficult to collect sufficient labeled samples for some practical engineering applications. To address this problem, a fault diagnosis method for rotating machinery with semi-supervised graph convolutional network (SSGCN) and two-dimensional (2-D) images converted from vibration signals is proposed in this paper. First, the one-dimensional (1-D) vibration signals are converted into 2-D images, by the no-parameter adjacency symmetrized dot pattern (NPASDP) method in polar coordinate system and the grayscale matrix mapping (GMM) method in Cartesian coordinate system. The local features of all samples are fully extracted using image processing methods in different coordinate systems to better characterize the relationship between different samples. Second, the normalized cross-correlation coefficient (NCC) is used to calculate the images similarity. Using each sample as a node, the graph structure is constructed by judging the adjacency relationship between all nodes through the similarity threshold. Finally, the SSGCN model is established to realize fault diagnosis. A bearing dataset and a rotor dataset with few data labels are used for validation, and the proposed method can get satisfactory fault diagnosis accuracy. The results show that the method can effectively improve the accuracy of model fault diagnosis with few labeled data.

Simple, Fast and Convenient Magnetic Bead-Based Sample Preparation for Detecting Viruses via Raman-Spectroscopy.

We introduce a magnetic bead-based sample preparation scheme for enabling the Raman spectroscopic differentiation of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2)-positive and -negative samples. The beads were functionalized with the angiotensin-converting enzyme 2 (ACE2) receptor protein, which is used as a recognition element to selectively enrich SARS-CoV-2 on the surface of the magnetic beads. The subsequent Raman measurements directly enable discriminating SARS-CoV-2-positive and -negative samples. The proposed approach is also applicable for other virus species when the specific recognition element is exchanged. A series of Raman spectra were measured on three types of samples, namely SARS-CoV-2, Influenza A H1N1 virus and a negative control. For each sample type, eight independent replicates were considered. All of the spectra are dominated by the magnetic bead substrate and no obvious differences between the sample types are apparent. In order to address the subtle differences in the spectra, we calculated different correlation coefficients, namely the Pearson coefficient and the Normalized cross correlation coefficient. By comparing the correlation with the negative control, differentiating between SARS-CoV-2 and Influenza A virus is possible. This study provides a first step towards the detection and potential classification of different viruses with the use of conventional Raman spectroscopy.

Fast Localization and High Accuracy Recognition of Tire Surface Embossed Characters Based on CNN

To solve the problem of recognizing artificial tire-side pressure printing characters with low efficiency and high labor intensity, we propose a CNN-based method for tire surface character recognition. In the image pre-processing, the SSR algorithm is improved to enhance the contrast of characters, and the Normalized Cross Correlation template matching algorithm based on pyramid acceleration is proposed to quickly locate the “DOT” characters and segment them. The improved LeNet-5 network structure is used to recognize characters, and a self-built digital sample library is randomly divided according to the ratio of 8:2 to conduct digital recognition experiments. The experimental results show that the recognition accuracy of the training set can reach 95.9%, and the accuracy of the validation set is 99.5%. The accuracy of the testing set is 95.6%, which meets the practical application requirements. Moreover, the whole algorithm only needs to be implemented on a commonly configured CPU, reducing equipment costs.

Measuring landslide-driven ground displacements with high-resolution surface models and optical flow

This study proposes and evaluates the performance of two image matching algorithms applied to hillshades derived from consecutive high-resolution digital surface models (DSMs) to measure surface displacements on landslides. The method is applied on Te Horo, a slow-moving landslide in the Dart Valley of New Zealand’s Southern Alps/Kā Tiritiri o te Moana using pairs of hillshades derived from Airborne and Satellite Photogrammetric Mapping (APM/SPM) of imagery captured in 2018 and 2020. This novel approach uses the consistency of displacement predictions generated from multiple hillshade pairs to gauge match quality and mask unreliable displacement predictions. The study compares a widely used normalised cross-correlation algorithm (NCC) alongside an optical flow approach for image matching. The performances of both algorithms are assessed against manually derived displacements of prominent surface features. We demonstrate the effectiveness of the masking approach as well as the good performance of the optical flow algorithm in delivering dense, accurate displacement measurements efficiently, particularly when high resolution DSMs are available. The results show that the main translational body of the landslide was displaced at rates averaging 25–55 mm day−1 over the 2018–2020 period.

Shoeprint image retrieval and crime scene shoeprint image linking by using convolutional neural network and normalized cross correlation

A shoeprint image retrieval process aims to identify and match images of shoeprints found at crime scenes with shoeprint images from a known reference database. It is a challenging problem in the forensic discipline of footwear analysis because a shoeprint found at the crime scene is often imperfect. Recovered shoeprints may be partial, distorted, left on surfaces that do not mark easily, or perhaps come from shoes that do not transfer marks easily. In this study, we present a shoeprint retrieval method by using a convolutional neural network (CNN) and normalized cross-correlation (NCC). A pre-trained CNN was used to extract features from the pre-processed shoeprint images. We then employed NCC to compute a similarity score based on the extracted image features. We achieved a retrieval accuracy of 82% in our experiments, where a “successful” retrieval means that the ground truth image was returned in the top 1% of returned images. We also extend our shoeprint retrieval method to the problem of linking shoeprints recovered from crime scenes. This new method can provide a linkage between two crime scenes if the two recovered shoeprints originated from the same shoe. This new method achieved a retrieval accuracy of 88.99% in the top 20% of returned images.

Cross-modality image translation: CT image synthesis of MR brain images using multi generative network with perceptual supervision

Background: Computed tomography (CT) and magnetic resonance imaging (MRI) are the mainstream imaging technologies for clinical practice. CT imaging can reveal high-quality anatomical and physiopathological structures, especially bone tissue, for clinical diagnosis. MRI provides high resolution in soft tissue and is sensitive to lesions. CT combined with MRI diagnosis has become a regular image-guided radiation treatment plan.Methods: In this paper, to reduce the dose of radiation exposure in CT examinations and ameliorate the limitations of traditional virtual imaging technologies, we propose a Generative MRI-to-CT transformation method with structural perceptual supervision. Even though structural reconstruction is structurally misaligned in the MRI-CT dataset registration, our proposed method can better align structural information of synthetic CT (sCT) images to input MRI images while simulating the modality of CT in the MRI-to-CT cross-modality transformation.Results: We retrieved a total of 3416 brain MRI-CT paired images as the train/test dataset, including 1366 train images of 10 patients and 2050 test images of 15 patients. Several methods (the baseline methods and the proposed method) were evaluated by the HU difference map, HU distribution, and various similarity metrics, including the mean absolute error (MAE), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). In our quantitative experimental results, the proposed method achieves the lowest MAE mean of 0.147, highest PSNR mean of 19.27, and NCC mean of 0.431 in the overall CT test dataset.Conclusions: In conclusion, both qualitative and quantitative results of synthetic CT validate that the proposed method can preserve higher similarity of structural information of the bone tissue of target CT than the baseline methods. Furthermore, the proposed method provides better HU intensity reconstruction for simulating the distribution of the CT modality. The experimental estimation indicates that the proposed method is worth further investigation.

Reflective Optical Tactile Sensor Based on Fiber Specklegram Analysis With the Capability of Contact Position Identification

A reflective tactile sensing scheme based on fiber specklegram analysis is proposed in this study. The specially designed sensor probe with a reflector inside was 3D printed from elastic material. A section of single mode fiber (SMF) and a section of multimode fiber (MMF) were embedded under the reflector. The light emitted from the SMF is reflected by the reflector and the reflected light illuminates the MMF. At the output facet of the MMF, specklegram is detected by a camera. The reflector consists of two soft mirrors that are perpendicular to each other originally. The contact force on the sensor probe causes the variation of the angle between the two mirrors. The transmission path of the light from SMF to MMF changes accordingly, which results in a changed specklegram. In this way, the zero-mean normalized cross-correlation coefficients of the fiber specklegrams decrease linearly with the increase of contact force. The sensitivity and measurement range of contact force are examined with MMFs of different diameters under two operating wavelengths. The negligible effect of fiber bending on the force sensing was also experimentally confirmed. In addition, the convolutional neural network is used to classify the specklegrams of three contact forces applied at nine positions of the sensor probe, and the average accuracy of classification is 100%, which shows its capability to identify the contact position on the probe. On the whole, the proposed tactile sensor is confirmed with a high sensitivity of force sensing in a proper measurement range and a capability to localize the contact position. The reflective structure also facilitates the integration with robot arm and other intelligent structures.

Inter-fraction deformable image registration using unsupervised deep learning for CBCT-guided abdominal radiotherapy

Objective. CBCTs in image-guided radiotherapy provide crucial anatomy information for patient setup and plan evaluation. Longitudinal CBCT image registration could quantify the inter-fractional anatomic changes, e.g. tumor shrinkage, and daily OAR variation throughout the course of treatment. The purpose of this study is to propose an unsupervised deep learning-based CBCT-CBCT deformable image registration which enables quantitative anatomic variation analysis. Approach. The proposed deformable registration workflow consists of training and inference stages that share the same feed-forward path through a spatial transformation-based network (STN). The STN consists of a global generative adversarial network (GlobalGAN) and a local GAN (LocalGAN) to predict the coarse- and fine-scale motions, respectively. The network was trained by minimizing the image similarity loss and the deformable vector field (DVF) regularization loss without the supervision of ground truth DVFs. During the inference stage, patches of local DVF were predicted by the trained LocalGAN and fused to form a whole-image DVF. The local whole-image DVF was subsequently combined with the GlobalGAN generated DVF to obtain the final DVF. The proposed method was evaluated using 100 fractional CBCTs from 20 abdominal cancer patients in the experiments and 105 fractional CBCTs from a cohort of 21 different abdominal cancer patients in a holdout test. Main Results. Qualitatively, the registration results show good alignment between the deformed CBCT images and the target CBCT image. Quantitatively, the average target registration error calculated on the fiducial markers and manually identified landmarks was 1.91 ± 1.18 mm. The average mean absolute error, normalized cross correlation between the deformed CBCT and target CBCT were 33.42 ± 7.48 HU, 0.94 ± 0.04, respectively. Significance. In summary, an unsupervised deep learning-based CBCT-CBCT registration method is proposed and its feasibility and performance in fractionated image-guided radiotherapy is investigated. This promising registration method could provide fast and accurate longitudinal CBCT alignment to facilitate inter-fractional anatomic changes analysis and prediction.

Comparison of Stereo Matching Algorithms for the Development of Disparity Map

Stereo Matching is one of the classical problems in computer vision for the extraction of 3D information but still controversial for accuracy and processing costs. The use of matching techniques and cost functions is crucial in the development of the disparity map. This paper presents a comparative study of six different stereo matching algorithms including Block Matching (BM), Block Matching with Dynamic Programming (BMDP), Belief Propagation (BP), Gradient Feature Matching (GF), Histogram of Oriented Gradient (HOG) and Fixed-Window Aggregated Cost (FWAC). In addition, three cost functions, namely, Mean Squared Error (MSE), Sum of Absolute Differences (SAD), and Normalized Cross-Correlation (NCC) were utilized and compared. The stereo images used in this study were obtained from the Middlebury Stereo Datasets provided with perfect and imperfect calibrations. It was observed that the selection of matching function is quite important and also depends on the image properties. Results showed that the BP algorithm in most cases provided better results achieving accuracies over 95%. Accordingly, BP algorithm is highly recommended based on rapidity and performance and for applications with the need of detection of small details, HOG is advised.

Simulation of X-ray projections on GPU: Benchmarking gVirtualXray with clinically realistic phantoms

Background and objectives: This study provides a quantitative comparison of images created using gVirtualXray (gVXR) to both Monte Carlo (MC) and real images of clinically realistic phantoms. gVirtualXray is an open-source framework that relies on the Beer-Lambert law to simulate X-ray images in realtime on a graphics processor unit (GPU) using triangular meshes.Methods: Images are generated with gVirtualXray and compared with a corresponding ground truth image of an anthropomorphic phantom: (i) an X-ray projection generated using a Monte Carlo simulation code, (ii) real digitally reconstructed radiographs (DRRs), (iii) computed tomography (CT) slices, and (iv) a real radiograph acquired with a clinical X-ray imaging system. When real images are involved, the simulations are used in an image registration framework so that the two images are aligned.Results: The mean absolute percentage error (MAPE) between the images simulated with gVirtualXray and MC is 3.12%, the zero-mean normalised cross-correlation (ZNCC) is 99.96% and the structural similarity index (SSIM) is 0.99. The run-time is 10 days for MC and 23 ms with gVirtualXray. Images simulated using surface models segmented from a CT scan of the Lungman chest phantom were similar to (i) DRRs computed from the CT volume and (ii) an actual digital radiograph. CT slices reconstructed from images simulated with gVirtualXray were comparable to the corresponding slices of the original CT volume.Conclusions: When scattering can be ignored, accurate images that would take days using MC can be generated in milliseconds with gVirtualXray. This speed of execution enables the use of repetitive simulations with varying parameters, e.g. to generate training data for a deep-learning algorithm, and to minimise the objective function of an optimisation problem in image registration. The use of surface models enables the combination of X-ray simulation with real-time soft-tissue deformation and character animation, which can be deployed in virtual reality applications.

Statistical Test of Detrended Multiple Moving Average Cross-Correlation Analysis and Its Application in Financial Market

In this paper, we first proposed a statistical test for the detrended multiple moving average cross-correlation coefficient [Formula: see text]. The [Formula: see text] mainly was used to analyze the correlation between the dependent variable y and other n independent variables [Formula: see text]. We proved that [Formula: see text] approximately obeys the chi-square distribution. We studied the statistical properties of the [Formula: see text] between normally distributed random sequences and power-law [Formula: see text] long memory random sequences. Furthermore, we discussed the influence of the cross-correlation among the target variable and independent variables on [Formula: see text]. Finally, we further study the application of [Formula: see text] to China’s stock markets and China carbon emission trading markets to investigate multiple cross-correlation. The empirical results show that there is a strong multiple correlation between China’s Shanghai, Shenzhen and Hong Kong stock markets, while the correlation between China’s carbon markets is not significant. This paper provides new ideas and theoretical support for exploring the correlation between multiple variables, which has implications for investors and policymakers.