Normalized Cross Correlation Research Articles

ECG classification using multifractal detrended moving average cross-correlation analysis

Accurate detection of arrhythmia signal types is of great significance for the early detection of heart disease and its subsequent treatment. The primary purpose of this study is to explore an electrocardiogram (ECG) classification system to improve its performance and achieve excellent computing performance, especially for large sample datasets. We classified ECG signals using the Hurst exponent, which is an ECG feature extracted by multifractal detrended moving average cross-correlation analysis (MF-XDMA). In addition, we used multifractal methods such as multifractal detrended fluctuation analysis (MF-DFA), multifractal detrended cross-correlation analysis (MF-DCCA) and multifractal detrended moving average (MF-DMA) to extract the features of ECG signals, and we used a support vector machine (SVM) to classify the four types of feature data. The experimental results show that MF-XDMA-SVM has the best classification performance for atrial premature beat (APB) and bigeminy signals, which indicates that MF-XDMA-SVM is the most effective for the extraction of ECG signal sequence features among the four multifractal models.

Anatomically constrained deformable 3D reconstruction of intraoperative uterus from preoperative MRI data on uterine fibroid treatment

ABSTRACT Detections of the uterus using preoperative magnetic resonance imaging (MRI) data are required for intraoperative navigation in High-Intensity Focused Ultrasound (HIFU) treatment of uterine fibroids. The modelling of the intraoperative uterus between empty and full bladder anatomy deems to be a crucial step to quantify uterus deformation caused by the bladder-and-rectum-filling (BRF) technique. In this paper, a novel two-stage conditional generative adversarial network (cGAN) is proposed to perform intraoperative uterus deformable reconstruction with anatomical constraints using only single preoperative MRI data, viz. ACDeformRec. The highly constrained anatomy properties are further captured by a novel attention network that formulates high-level uterus segmentation task by incorporating correlations of surrounding organs for more anatomically plausible reconstruction. The experimental result demonstrates the robustness of ACDeformRec by evaluating its performance on 181 clinical datasets and has achieved the lowest reconstruction error of 0.735 ± 0.045 mm with Dice Similarity Coefficient of 94.23% and Normalised Cross Correlation of 97.23%.

Normalized Cross Correlation Template Matching for Oil Palm Tree Counting from UAV image

Tree crown detection and counting from remote sensing data such as images from Unmanned Aerial Vehicle (UAV) shows significant role in this modern era for vegetation monitoring. Since the data processing would depends on raw data available and for this case the RGB data, thus a suitable method such as template matching is presented. Normalized cross correlation is widely used as an effective measure in similarity between template image and the source or testing images. This paper focuses on six (6) steps involved in the overall process which are: (1) image acquisition, (2) template optimisation, (3) normalized cross correlation, (4) sliding window, (5) matched image and counting, and (6) accuracy assessment. Normalized cross correlation and sliding window techniques proposed for this work resulted in 80% to 89% F-measure values. This result indicates that UAV image data with appropriate image processing method/s have the potential to provide vital information for oil palm tree counting. This would be beneficial in plantation management to estimate yield and productivity. However, there are still rooms for improvement to achieve better results.

Integral images-based approach for fabric defect detection

Normalized cross correlation (NCC) technique is the most accurate and robust in defect detection techniques in industry. But its robustness is sensitive against change lighting and the computational cost is very expensive. This paper presents a new approach to the fabric defect detection problem using integral images scheme. The integral image technique is used to substitute the huge operations in NCC to speed up the calculation process. So, the defect detection process can be done in real time. The integration process improves the robustness against different types of noises. The performance of the proposed approach is evaluated by using three different datasets of fabric images with different types of fabric defects. Experimental results demonstrate the proposed approach is efficient and effective for detecting defects in flawed fabric in real time. As a result, obtaining correlation values between windows through integral image transform has reduced computation cost by 98.78% and has successfully provided the detection rate of the defects by 97.5% at minimum.

Deep learning-based thoracic CBCT correction with histogram matching

Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used for daily delivery of radiation therapy, especially for stereotactic body radiation therapy (SBRT), which imposes particularly high demands for setup accuracy. The clinical applications of CBCTs are constrained, however, by poor soft tissue contrast, image artifacts, and instability of Hounsfield unit (HU) values. Here, we propose a new deep learning-based method to generate synthetic CTs (sCT) from thoracic CBCTs. A deep-learning model which integrates histogram matching (HM) into a cycle-consistent adversarial network (Cycle-GAN) framework, called HM-Cycle-GAN, was trained to learn mapping between thoracic CBCTs and paired planning CTs. Perceptual supervision was adopted to minimize blurring of tissue interfaces. An informative maximizing loss was calculated by feeding CBCT into the HM-Cycle-GAN to evaluate the image histogram matching between the planning CTs and the sCTs. The proposed algorithm was evaluated using data from 20 SBRT patients who each received 5 fractions and therefore 5 thoracic CBCTs. To reduce the effect of anatomy mismatch, original CBCT images were pre-processed via deformable image registrations with the planning CT before being used in model training and result assessment. We used planning CTs as ground truth for the derived sCTs from the correspondent co-registered CBCTs. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC) indices were adapted as evaluation metrics of the proposed algorithm. Assessments were done using Cycle-GAN as the benchmark. The average MAE, PSNR, and NCC of the sCTs generated by our method were 66.2 HU, 30.3 dB, and 0.95, respectively, over all CBCT fractions. Superior image quality and reduced noise and artifact severity were seen using the proposed method compared to the results from the standard Cycle-GAN method. Our method could therefore improve the accuracy of IGRT and corrected CBCTs could help improve online adaptive RT by offering better contouring accuracy and dose calculation.

Correlation Properties of Sea Surface Images on Video Stream Frames

The work is devoted to the characteristic properties analysis of the sea surface image on video stream frames, which make it possible to make a decision about the detection of object images on them in the absence of a priori information about objects, which is important in the development of navigation systems, security and surveillance systems. In this paper, it is proposed to analyze the values of the normalized cross-correlation coefficients of a given image fragment with higher dimension fragments on sequential video stream frames to identify the distinctive properties of agitated sea surface and floating objects images. The computational experiments were carried out and have shown that the analysis of the results of calculating the frame fragments cross-correlations allows us to estimate the amount of displacement and distortion of a given image fragment. The results of computational experiments demonstrate the presence of differences in the values of the corresponding cross-correlation coefficients for the sea surface images with different agitation degrees, containing and not containing an image of the object. Based on the analysis of the proposed correlation properties of the sea surface images, a decisive rule for selecting fragments of frames containing an image of an object is formulated, the use of which, in many cases, allows to detect fragments of object images correctly.

Unsupervised Learning-Based CBCT-CT Deformable Image Registration for CBCT-Guided Abdominal Radiotherapy

Daily Cone beam CT (CBCT) imaging provides necessary anatomical information for accurate patient setup. Image quality of CBCT is usually far inferior to simulation CT scans. A workaround is to register the CT to the CBCT such that the contours and Hounsfield Unit (HU) values of the CT can be propagated to the CBCT. However, the inconsistent HU values across CT and CBCT make it less effective to use conventional image similarity measures. We aim to develop an unsupervised registration network to overcome this challenge in multimodal CT-CBCT image registration.We propose to integrate directional local structural similarity into an unsupervised learning framework to perform abdominal CT-CBCT image registration. Directional local structural similarity measures the image's self-similarity which reflects the underlying structural similarity regardless of the modality in use. The CBCT and CT images were separately processed to extract directional local structural similarity feature maps in different directions. We concatenated the directional local structural similarity feature maps and the original images as network input. Taking both the original images and their respective structural similarity feature maps as input allows the network to fully explore the potential correlations between CBCT and CT for accurate deformation vector field (DVF) prediction. Salient features learnt through previous iterations were highlighted by attention gates across layers to expedite the learning process. A 3D bicubic interpolation was used to up-sample and smooth the predicted DVF. We performed a leave-one-out cross validation with an image dataset of 45 patients to evaluate the proposed registration method. Normalized cross correlation (NCC) and target registration error (TRE) between CBCT and deformed CT were calculated to quantify the registration accuracy.Our results show that the alignment between the abdominal soft tissues has been greatly improved after registration for all patients. The mean and standard deviation of NCC and TRE were 0.97 (range 0.95-0.99) and 1.88 (range 1.03-2.67) mm. The proposed network allows for many datasets to be used as training datasets since ground truth DVF is not needed for the training process. The proposed network can predict a final DVF via a single forward prediction, which is faster than the conventional iterative registration algorithms.We have developed a novel unsupervised multimodal image registration method for CT-CBCT abdominal image registration, which does not need ground truth DVF for training, and demonstrated its feasibility. Taking both CBCT and CT images and their respective directional local structural similarity features as input, the proposed network performs direct DVF prediction to register the abdominal CT to CBCT images. This tool will be useful for future CBCT-guided radiotherapy of abdominal malignancies.

Automatic Extraction of Interior Orientation Data in Aerial Photography Using Image Matching Method

The Interior Orientation is a set of parameters that have been determined to transform the coordinates of the camera photo. The pixel coordinates of Fiducial Mark in the base image (Search window) are obtained automatically. The template of the fiducial mark is designed on single-frame aerial photographs. The concept of photogrammetry with Image Matching techniques is applied in the programming works. The Normalized Cross-Correlation (NCC) method coupling with the Area Based Matching technique is precisely used in the automatic computation for measuring the coordinate of fiducial marks. Herein, three templates are provided for this calculation. The coordinates from the manual rectification are employed. The results reveal that the third template is more accurate than the others with the RMSE value of 0.0066. The accuracy regarding the results of manual rectification depends on the operator when they are identifying for pixel values.

Machine learning virtual SEM metrology and SEM-based OPC model methodology

Background: E-beam metrologies, both critical dimension scanning electron microscope metrology and defect scan metrology, have been playing a very critical role in gating patterning quality. SEM images can provide rich visual information for engineers to do qualitative and quantitative analyses. However, the low e-beam metrology tool throughput makes it impossible to obtain SEM images for larger area. Monte Carlo-based SEM image simulations or other SEM image simulations require postlithography or postetch pattern three-dimensional structures as prerequisite, and the simulation speed is not sufficiently fast for full chip implementation. Aim: We aim to develop machine learning SEM models with sufficient accuracy and speed for full chip application in semiconductor manufacturing environment. Approach: We have proposed a virtual SEM metrology solution based on U-Net neural network with physics-based feature maps as model input. With information in aerial image space encoded properly, SEM images of both postlithography and postetch can be predicted accurately enough for practical applications using our proposed virtual SEM metrology models. Equipped with GPUs, the machine learning-based SEM image models are fast enough to make it possible to realize full chip SEM generation from post-OPC data. Results: Our machine learning SEM image models can predict SEM images with normalized cross correlation around 0.95 in reference to ground truth SEM images, each SEM image (512 × 512 in size) takes about 800 ms using single CPU, and the speed can be accelerated to about 10 ms with single GPU. Conclusions: Using U-Net structure and physics-based feature maps as model inputs, machine learning-based SEM image models can be developed. The models are sufficiently accurate and fast to find their applications in semiconductor manufacturing, and they can be used as independent model for OPC data verification or generate SEM images as reference for SEM defect scan metrology.

HSV model based data hiding in video for watermark applications

This manuscript proposes a new data hiding approach that is used in watermark applications in video by transforming the RGB model to HSV model. This method initially estimates the number of frames needed to embed the data (watermark). Then two sets of RGB (Red, Green, Blue) coefficients (R1, G1, B1), (R2, G2, B2) are converted to HSV (Hue, saturation, values) Coefficients (H1, S1, V1) and (H2, S2, V2). The ‘Value’ Coefficients V1 and V2 are used to embed the watermark, since there exists a strong correlation between the adjacent ‘Value’ Coefficients. The same process is repeated on adjacent HSV coefficients till the watermark is fully embedded. After embedding the data HSV coefficients are again converted back to RGB coefficients. During the extraction phase, the data is extracted by transforming the RGB coefficient to HSV coefficients. One bit of information can be extracted from two adjacent HSV coefficients. Experimental outcomes show that the proposed watermarking approach is efficiently against attacks, viz noise, filtering, etc. Also, the proposed method performs better than traditional watermarking methods with the help of embedding rate (bpp), Structural similarity index measurement (SSIM), Visual quality (PSNR), Normalized cross-correlation (NC).

Automatic Extraction Of Interior Orientation Data In Aerial Photography Using Image Matching Method

The Interior Orientation is a set of parameters that have been determined to transform the coordinates of the camera photo, that is the coordinates of the pixel leading to the coordinates of the image. This parameter is used to calibrate the camera before use so as to produce a precise measurement from an aerial photograph. This orientation parameter consists of a calibrated and equivalent camera focal length, lens distortion, principal point, fiducial mark location, camera resolution, and flatness of the focal plane. All of these parameters are attached to or contained on the camera sensor and the values of these parameters can usually be known from the camera's report page. In this work, the author wants to obtain pixel coordinates from the Fiducial Mark in the base image (Window Search) automatically, therefore a Fiducial Mark template was created which is formed from a piece of a photo image frame to determine the Fiducial Mark coordinate values from the base image ( Window Search), the basis of this programming is to use the concept of photogrammetry, which uses Image Matching techniques. The Image Matching process was developed from the C ++ Language programming algorithm platform, this was done in order to speed up computational results. There are a number of techniques for doing Image Matching, in this study the authors conducted using the Normalized Cross-Correlation Image Matching. In statistics Normalized Cross-Correlation is between two random variables by determining the size of how closely the two variables are different simultaneously. Similarly, Normalized Cross-Correlation in Image Matching is a measurement by calculating the degree of similarity between two images. This level of similarity is determined by Normalized Cross-Correlation (NCC). The Least Square Image Matching method is used to increase the accuracy of the coordinates of the conjugation points.

Synthetic Computed Tomography Generation from 0.35T Magnetic Resonance Images for Magnetic Resonance–Only Radiation Therapy Planning Using Perceptual Loss Models

PurposeMagnetic resonance imaging (MRI) provides excellent soft-tissue contrast, which makes it useful for delineating tumor and normal structures in radiation therapy planning, but MRI cannot readily provide electron density for dose calculation. Computed tomography (CT) is used but introduces registration uncertainty between MRI and CT. Previous studies have shown that synthetic CTs (sCTs) can be generated directly from MRI images with deep learning methods. However, mainly high-field MRI images have been validated. This study tested whether acceptable sCTs for MR-only radiation therapy planning can be synthesized using an integrated MR-guided linear accelerator at 0.35T, using MRI images and treatment plans in the liver region. Methods and MaterialsTwo models were investigated in this study: a convolutional neural network (Unet) with conventional mean square error (MSE) loss and a Unet using a secondary convolutional neural network for perceptual loss. A total of 37 cases were used in this study with 10-fold cross validation, and 37 treatment plans were generated and evaluated for target coverage and dose to organs at risk (OARs) in the MSE loss model, perceptual loss model, and original CT. ResultsThe sCTs predicted by the perceptual loss model had improved subjective visual quality compared with those predicted by the MSE loss model, but both were similar in mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). The MAE, PSNR, and NCC for the perceptual loss model were 35.64, 24.11, and 0.9539, respectively, and those for the MSE loss model were 35.67, 24.36, and 0.9566, respectively. No significant differences in target coverage and dose to OARs were found between the sCT predicted by the perceptual loss model or by the MSE model and the original CT image. ConclusionsThis study indicated that a Unet with both MSE loss and perceptual loss models can be used for generating sCT images from a 0.35T integrated MR linear accelerator.

An Analysis of Surface Ocean Currents from HF Radar Measurements in the Bali Strait and the Flores Sea, Indonesia

<p class='IJASEITAbtract'>Analyze Surface Ocean Currents (SOCs) with one year of HF Radar data (2018-2019) for each season to determine the characteristics of the SOC direction and speed of the crossing route and its control factors carried out in the Bali Strait and the Flores Sea. Method of data analysis by computing the SOC speed and direction of the zonal and meridional components. The results showed that the SOC pattern in the Bali Strait affects the season where its speed in the DJF season is lower than the JJA season. Moreover, the SOC direction in the Bali Strait is dominant towards the south due to the influence of bathymetry. Meanwhile, the SOC pattern in the Flores Sea has a random pattern every season for the influence of topography in the form of small islands that influence the SOC dominant pattern. Furthermore, the SOC characteristics on the Bali Strait crossing route throughout the month are divided into two patterns: random on the eastern side of East Java Island and dominant towards the south on the west side of Bali Island with a maximum speed of 83 cm/s. Meanwhile, the crossing route in the Flores Sea is random, with a maximum speed of 32 cm/s. Whereas, based on the normal cross-correlation method, the SOC control factors in the Bali Strait tend to be influenced by tides, while the factors in the Flores Sea are less influential based on the distribution of zonal and meridional currents of HF Radar.

Scalable quorum-based deep neural networks with adversarial learning for automated lung lobe segmentation in fast helical free-breathing CTs.

Fast helical free-breathing CT (FHFBCT) scans are widely used for 5DCT and 5D Cone Beam imaging protocols. For quantitative analysis of lung physiology and function, it is important to segment the lung lobes in these scans. Since the 5DCT protocols use up to 25 FHFBCT scans, it is important that this segmentation task be automated. In this paper, we present a deep neural network (DNN) framework for segmenting the lung lobes in near real time. A total of 22 patient datasets (550 3D CT scans) were used for the study. Each of the lung lobes was manually segmented and considered ground-truth. A supervised and constrained generative adversarial network (CGAN) was employed for learning each set of lobe segmentations for each patient with 12 patients designated for training data. The resulting generator DNNs represented the lobe segmentations for each training dataset. A quorum-based algorithm was then implemented to test validation data consisting of 10 separate patient datasets (250 3D CTs). Each of the DNNs predicted their corresponding lobes for the validation data, and equal weights were given to the 12 generator CGANs. The quorum process worked by selecting the weighted average result of all 12 CGAN results for each lobe. When evaluated against ground-truth segmentations, the quorum-based lobe segmentation was observed to have average structural similarity index, normalized cross-correlation coefficient, and dice coefficient values of 0.929, 0.806, and 0.814, respectively, compared to values of 0.911, 0.698, and 0.696, respectively, using a conventional strategy. The proposed quorum-based approach computed segmentations with clinically acceptable accuracy in near real time using a multi-GPU-based computing setup. This method is scalable as more patient-specific CGANs can be added to the quorum over time.

Method of extracting control points from GF-7 remote sensing image based on five-layer-fifteen-level tiles

In view of the traditional manual and semi-automatic methods can not quickly and effectively extract control points, this paper uses five-layer fifteen-level tiles (FLFLT) as reference images, and proposes an efficient and automatic method for automatic extraction control points of GF-7 image. Firstly, the remote sensing image and reference image are sampled down, and the remote sensing image is partitioned to improve the image processing efficiency. The Harris algorithm is used to extract the feature points of the remote sensing image and reference image, the normalized cross-correlation (NCC) algorithm is used for feature matching, and the Random Sampling Consistent (RANSAC) algorithm is used for gross error elimination. Finally, the least-square algorithm was used to fit the geometric transformation parameters, and the geometric transformation model was used to carry out geometric correction of GF-7 image, and the better correction results were achieved. Experimental results show that the proposed algorithm can extract control points quickly and effectively, and can be used for automatic extraction and geometric correction of high resolution satellite data.

FPSN-FNCC: an accurate and fast motion tracking algorithm in 3D ultrasound for image-guided interventions

The uncertain motions of a target caused by the breath, heartbeat and body drift of a patient can increase the target locating error during image-guided interventions, and that may cause additional surgery trauma. A surgery navigation system with accurate motion tracking is important for improving the operation accuracy and reducing trauma. In this work, we propose an accurate and fast tracking algorithm in three-dimensional (3D) ultrasound (US) sequences for US-guided surgery to achieve moving object tracking. The idea of this algorithm is as follows. Firstly, feature pyramid architecture is introduced into a Siamese network to extract multiscale convolutional features. Secondly, to improve the network discriminative power and the robustness to ultrasonic noise and gain variation, we use the normalized cross correlation (NCC) to calculate the similarity between template block and search block. Thirdly, a fast NCC (FNCC) is proposed, which can perform the real-time tracking. Finally, a density peaks clustering approach is used to compensate the motion of the target and further improve the tracking accuracy. The proposed algorithm is evaluated on a CLUST dataset that includes 22 sets of 3D US sequences, and the mean error of 1.60 0.97 mm compared with manual annotations is obtained. After comparing with other published works, the results show that our algorithm achieves the comparable performance. The ablation study proves that the results benefit from the feature pyramid architecture and FNCC. These findings show that our algorithm may improve the motion tracking accuracy in image-guided interventions.

QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study

The purpose of this study was to directly and quantitatively measure BMD from Cone-beam CT (CBCT) images by enhancing the linearity and uniformity of the bone intensities based on a hybrid deep-learning model (QCBCT-NET) of combining the generative adversarial network (Cycle-GAN) and U-Net, and to compare the bone images enhanced by the QCBCT-NET with those by Cycle-GAN and U-Net. We used two phantoms of human skulls encased in acrylic, one for the training and validation datasets, and the other for the test dataset. We proposed the QCBCT-NET consisting of Cycle-GAN with residual blocks and a multi-channel U-Net using paired training data of quantitative CT (QCT) and CBCT images. The BMD images produced by QCBCT-NET significantly outperformed the images produced by the Cycle-GAN or the U-Net in mean absolute difference (MAD), peak signal to noise ratio (PSNR), normalized cross-correlation (NCC), structural similarity (SSIM), and linearity when compared to the original QCT image. The QCBCT-NET improved the contrast of the bone images by reflecting the original BMD distribution of the QCT image locally using the Cycle-GAN, and also spatial uniformity of the bone images by globally suppressing image artifacts and noise using the two-channel U-Net. The QCBCT-NET substantially enhanced the linearity, uniformity, and contrast as well as the anatomical and quantitative accuracy of the bone images, and demonstrated more accuracy than the Cycle-GAN and the U-Net for quantitatively measuring BMD in CBCT.

UAV based accurate displacement monitoring through automatic filtering out its camera's translations and rotations

In this paper, three translations and three rotations of the unmanned aerial vehicle (UAV) were derived through the suggested Normalized Cross Correlation (NCC) based template matching computer vision method and their effect on the monitored structural displacement is analyzed. The NCC-based template matching is used to obtain the vibration of UAVs by tracing far-field background points at the recorded images. The images captured at any general orientations are rotated back to their original positions which could detect errors in the monitored displacement induced by the rotation angles. A fast NCC-based template matching method was also proposed in this paper, which could accelerate the original NCC-based template matching method significantly and reaches the same level accuracy as that of the original NCC-based template matching method. In order to verify the concept, a series of experiments were performed on a Multipurpose Testing System (MTS) machine with amplitudes of 10 mm, 5 mm, and 2.5 mm and frequencies of 2 Hz, 1 Hz and 0.5 Hz, respectively, at the same time a UAV and a fixed camera were used to record the motions of the MTS piston. The derived displacements through the UAV and the fixed camera are compared with the true motions of the MTS machine. Excellent precision and consistence were obtained for the UAV monitored displacement, the MTS piston motion, and the fixed camera derived displacement. Comparing to the case of the fixed camera, scales and rotation angles of the UAV are critical and largely affect the accuracy of monitored displacements. A field experiment was conducted to test the proposed method in a windy environment. Excellent agreement was found between the monitored displacement obtained by the UAV and that derived from the fixed camera.

An optimization of color halftone visual cryptography scheme based on Bat algorithm

Abstract Visual cryptography is a cryptographic technique that allows visual information to be encrypted so that the human optical system can perform the decryption without any cryptographic computation. The halftone visual cryptography scheme (HVCS) is a type of visual cryptography (VC) that encodes the secret image into halftone images to produce secure and meaningful shares. However, the HVC scheme has many unsolved problems, such as pixel expansion, low contrast, cross-interference problem, and difficulty in managing share images. This article aims to enhance the visual quality and avoid the problems of cross-interference and pixel expansion of the share images. It introduces a novel optimization of color halftone visual cryptography (OCHVC) scheme by using two proposed techniques: hash codebook and construction techniques. The new techniques distribute the information pixels of a secret image into a halftone cover image randomly based on a bat optimization algorithm. The results show that these techniques have enhanced security levels and make the proposed OCHVC scheme more robust against different attacks. The OCHVC scheme achieves mean squared error (MSE) of 95.0%, peak signal-to-noise ratio (PSNR) of 28.3%, normalized cross correlation (NCC) of 99.4%, and universal quality index (UQI) of 99.3% on average for the six shares. Subsequently, the experiment results based on image quality metrics show improvement in size, visual quality, and security for retrieved secret images and meaningful share images of the OCHVC scheme. Comparing the proposed OCHVC with some related works shows that the OCHVC scheme is more effective and secure.

Sensitivity of quantitative symmetry measurement algorithms for convergent beam electron diffraction technique

We investigate the sensitivity of symmetry quantification algorithms based on the profile R-factor (Rp) and the normalized cross-correlation (NCC) coefficient (γ). A DM (Digital Micrograph©) script embedded in the Gatan digital microscopy software is used to develop the symmetry quantification program. Using the Bloch method, a variety of CBED patterns are simulated and used to investigate the sensitivity of symmetry quantification algorithms. The quantification results show that two symmetry quantification coefficients are significantly sensitive to structural changes even for small strain values of < 1%.