Normalized Cross-correlation Method Research Articles

Accurate Recognition of Vascular Lumen Region from 2D Ultrasound Cine Loops for Bubble Detection.

Accurate identification of vascular lumen region founded the base of bubble detection and bubble grading, which played a significant role in the detection of vascular gas emboli for the diagnosis of decompression sickness. To assist in the detection of vascular bubbles, it is crucial to develop an automatic algorithm that could identify vascular lumen areas in ultrasound videos with the interference of bubble presence. This article proposed an automated vascular lumen region recognition (VLRR) algorithm that could sketch the accurate boundary between vessel lumen and tissues from dynamic 2D ultrasound videos. It adopts 2D ultrasound videos of the lumen area as input and outputs the frames with circled vascular lumen boundary of the videos. Normalized cross-correlation method, distance transform technique, and region growing technique were adopted in this algorithm. Results A double-blind test was carried out to test the recognition accuracy of the algorithm on 180 samples in the images of 6 different grades of bubble videos, during which, intersection over union and pixel accuracy were adopted as evaluation metrics. The average IOU on the images of different bubble grades reached 0.76. The mean PA on 6 of the images of bubble grades reached 0.82. It is concluded that the proposed method could identify the vascular lumen with high accuracy, potentially applicable to assist clinicians in the measurement of the severity of vascular gas emboli in clinics.

Optimization of Cross Correlation Algorithm for Annual Mapping of Alpine Glacier Flow Velocities; Application to Sentinel-2

Nowadays, satellite observations cover most of the Earth’s surface in a repetitive manner. This information is crucial for documenting variability and environmental changes such as glacier surface velocity. With this in mind, digital image processing has been developed and improved over the past decades. The processing challenges are now related to optimizing parameters that account for the high variability of natural processes, as well as filtering and aggregating the results to provide useful products to end-users. Based on the normalized cross-correlation (NCC) method applied to Sentinel-2 optical satellite observations up to 400 days apart, we present a series of tests to derive optimal parameter values for the quantification of alpine glacier ice velocity that we have applied to the Mont-Blanc massif where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements are available. We found that a search distance adapted to the temporal baseline, a 16×16 pixel window size and a 5×5 pixels sampling provide an appropriate combination of parameters to process Sentinel-2 with the NCC method when applied to small alpine glaciers. Combining several spatial and temporal filters applied to a large set of more than 18,000 displacement maps obtained between 2015 and 2020, then aggregating these filtered maps using statistical or linear regressions into annual maps, yields near-complete maps of the test region with a RMSE reduced to about 12 m.yr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> compared to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements.

Analysis Clustering Using Normalized Cross Correlation In Fuzzy C-Means Clustering Algorithm

Abstract: Fuzzy C-Means Clustering (FCM) has been widely known as a technique for performing data clustering, such as image segmentation. This study will conduct a trial using the Normalized Cross Correlation method on the Fuzzy C-Means Clustering algorithm in determining the value of the initial fuzzy pseudo-partition matrix which was previously carried out by a random process. Clustering technique is a process of grouping data which is included in unsupervised learning. Data mining generally has two techniques in performing clustering, namely: hierarchical clustering and partitional clustering. The FCM algorithm has a working principle in grouping data by adding up the level of similarity between pairs of data groups. The method applied to measure the similarity of the data based on the correlation value is the Normalized Cross Correlation (NCC). The methodology in this research is the steps taken to measure clustering performance by adding the Normalized Cross Correlation (NCC) method in determining the initial fuzzy pseudo-partition matrix in the Fuzzy C-Means Clustering (FCM) algorithm. the results of data clustering using the Normalized Cross Correlation (NCC) method on the Fuzzy C-Means Clustering (FCM) algorithm gave better results than the ordinary Fuzzy C-Means Clustering (FCM) algorithm. The increase that occurs in the proposed method is 4.27% for the Accuracy, 4.73% for the rand index and 8.26% for the F-measure..&#x0D;

Voice Controlled Comparator Improvement Based on Resource Utilization in SoC Ecosystem for Parking Assist System

This paper introduces the voice controlled comparator improvement for maneuvering a miniature electric vehicle based on the resource utilization in the system-on-chip (SoC) ecosystem. An intelligent parking assist is to support the driver outside a car while parking in the crowded locations. Voice controlled improvement based on the resource utilization on the SoC ecosystem is modified to command for moving vehicle. The normalized cross correlation (NCC) technique is proposed for voice controlled system with low utilization on the SoC ecosystem. Hardware and software co-design by the Xilinx VIVADO and Vitis software are used to design on an ARM multicore processor and field programmable gate array (FPGA) system inside a ‘Zedboard’ development board. We perform the experiments for Thai command word recognition via Bluetooth using the proposed NCC method to identify the basic command stored on SD card in Zedboard. Empirical results show the voice controlled improvement based on the Pearson’s correlation coefficient (PCC), modified PCC and proposed NCC methods on a Zedboard. The resource utilization on Zedboard are less than as 17.57% in look-up table (LUT), 29.12% in look-up table random access memory (LUTRAM), 6.44% in flip-flop (FF) and 2.38% in input/output (I/O) as compared with a ZYBO system. An average execution time of Zedboard using proposed NCC method is less than PCC and modified PCC as 5.12%, 1%, respectively. Results of proposed NCC of Thai voice command controlled show the validate workability at average percentage accuracy at 90% in the outdoor environments.

Iterative normalized cross-correlation method for absolute optical path difference demodulation of dual interferometers.

It is still a challenge to realize the absolute optical path difference (OPD) demodulation of multi-interference systems with a narrow spectral interval and small OPD interval. In this paper, an iterative normalized cross-correlation algorithm is firstly proposed for demodulating the multiple absolute OPDs of a dual-interference system and applied to optical fiber sensing system. By constructing a template function in combined form, the optimal solutions of its components and OPDs are solved iteratively based on the reconstruction matrix method and cross-correlation algorithm, respectively. The simulation and experiment show that the demodulation accuracies near the OPDs of 560 µm and 660 µm are both up to 5 nm in different spectral intervals from 45 to 80 nm. The simulation results show that all demodulation precisions at the spectral interval of 55 nm do not exceed 4 nm when the OPD changes in the range of 650-670 µm. Besides, the experimental verification shows the temperature accuracy (0.125 °C) with 95% confidence of T-distribution is very close to the control accuracy (0.1 °C). The proposed algorithm can improve the multiplexing capability of optical fiber sensor system and reduce its cost.

Multimodal 3D ultrasound and CT in image-guided spinal surgery: public database and new registration algorithms.

Accurate multimodal registration of intraoperative ultrasound (US) and preoperative computed tomography (CT) is a challenging problem. Construction of public datasets of US and CT images can accelerate the development of such image registration techniques. This can help ensure the accuracy and safety of spinal surgeries using image-guided surgery systems where an image registration is employed. In addition, we present two algorithms to register US and CT images. We present three different datasets of vertebrae with corresponding CT, US, and simulated US images. For each of the two latter datasets, we also provide 16 landmark pairs of matching structures between the CT and US images and performed fiducial registration to acquire a silver standard for assessing image registration. Besides, we proposed two patch-based rigid image registration algorithms, one based on normalized cross-correlation (NCC) and the other based on correlation ratio (CR) to register misaligned CT and US images. The CT and corresponding US images of the proposed database were pre-processed and misaligned with different error intervals, resulting in 6000 registration problems solved using both NCC and CR methods. Our results show that the methods were successful in aligning the pre-processed CT and US images by decreasing the warping index. The database provides a resource for evaluating image registration techniques. The simulated data have two applications. First, they provide the gold standard ground-truth which is difficult to obtain with ex vivo and in vivo data for validating US-CT registration methods. Second, the simulated US images can be used to validate real-time US simulation methods. Besides, the proposed image registration techniques can be useful for developing methods in clinical application.

Fast normalized cross-correlation for measuring distance to objects using optic flow, applied for helicopter obstacle detection

The fast normalized cross-correlation (NCC) calculation method presented here features low computational requirements, which makes it suitable for being implemented in real time onboard micro-controllers with very few computational resources. This method was adapted for making distance measurements, using a high speed optic flow sensor operating at 20m∕s. An application of this study is to develop a proof of concept of an innovative optic flow sensor fixed at the tip of an helicopter’s blade (from Airbus Helicopter) to measure the distance to various obstacles (wall, cliff...) in the azimuthal plane (rotor plane) during hovering flight. Due to its small size, this sensor developed in this paper can also be also adapted for Micro aerial vehicles (MAVs). This method of calculation requires less memory than the reference method, at the expense of some extra arithmetical operations, but it is still significantly lighter than the classical NCC method. An algorithm for implementing this method in real-time robotic applications is presented. Experimental results confirm the efficiency of this highly time-saving method.

Modeling Ultrasound Propagation in the Moving Brain: Applications to Shear Shock Waves and Traumatic Brain Injury.

Traumatic brain injury (TBI) studies on the living human brain are experimentally infeasible due to ethical reasons and the elastic properties of the brain degrade rapidly postmortem. We present a simulation approach that models ultrasound propagation in the human brain, while it is moving due to the complex shear shock wave deformation from a traumatic impact. Finite difference simulations can model ultrasound propagation in complex media such as human tissue. Recently, we have shown that the fullwave finite difference approach can also be used to represent displacements that are much smaller than the grid size, such as the motion encountered in shear wave propagation from ultrasound elastography. However, this subresolution displacement model, called impedance flow, was only implemented and validated for acoustical media composed of randomly distributed scatterers. Herein, we propose a generalization of the impedance flow method that describes the continuous subresolution motion of structured acoustical maps, and in particular of acoustical maps of the human brain. It is shown that the average error in simulating subresolution displacements using impedance flow is small when compared to the acoustical wavelength ( λ /1702). The method is then applied to acoustical maps of the human brain with a motion that is imposed by the propagation of a shear shock wave. This motion is determined numerically with a custom piecewise parabolic method that is calibrated to ex vivo observations of shear shocks in the porcine brain. Then the fullwave simulation tool is used to model transmit-receive imaging sequences based on an L7-4 imaging transducer. The simulated radio frequency data are beamformed using a conventional delay-and-sum method and a normalized cross-correlation method designed for shock wave tracking is used to determine the tissue motion. This overall process is an in silico reproduction of the experiments that were previously performed to observe shear shock waves in fresh porcine brain. It is shown that the proposed generalized impedance flow method accurately captures the shear wave motion in terms of the wave profile, shock front characteristics, odd harmonic spectrum generation, and acceleration at the shear shock front. We expect that this approach will lead to improvements in image sequence design that takes into account the aberration and multiple reflections from the brain and in the design of tracking algorithms that can more accurately capture the complex brain motion that occurs during a traumatic impact. These methods of modeling ultrasound propagation in moving media can also be applied to other displacements, such as those generated by shear wave elastography or blood flow.

Combined pattern matching and feature tracking for Bohai Sea ice drift detection using Gaofen-4 imagery

ABSTRACT Sea ice is the main cause of winter marine disasters in the Bohai Sea, which is an important sea area in China. The sea ice in the Bohai Sea is easily deformed by strong water currents and rapid temperature changes. Consequently, low spatial and temporal resolution data cannot meet the needs of high-precision monitoring of the sea ice in the Bohai Sea. The geostationary satellite Gaofen-4 (GF-4) has the unique advantages of a high temporal resolution (20 s) and a high spatial resolution (50 m) and can be used to monitor sea ice drift in the Bohai Sea. We evaluate a new algorithm that combines the scale-invariant feature transform (SIFT) technique and the normalized cross-correlation (NCC) method to obtain accurate sea ice drift information. The interclass separability method is used to determine the optimal band combination (OBC), and the results show that the OBC for distinguishing sea ice from sea water is B2×(B1× B5)−1. The proposed method improves noise immunity and calculation efficiency and considers the characteristics of ice blocks. The proposed algorithm can also obtain more correctly matched feature points than the NCC method and approximately 10 times more correct points than the SIFT technique. The mean absolute error (MAE) of the velocity obtained with the proposed algorithm is 0.1615 m s−1 lower than those obtained with the SIFT and NCC methods, and the root mean square error of the velocity determination of the three algorithms is very small. The MAE of the ice movement direction obtained with the proposed algorithm is 0.605° lower than those obtained with the SIFT and NCC methods, and the MAE of a second data set is 12.378° better than that of the SIFT method. The motion of sea ice obtained by the proposed algorithm is close to the actual movement of the sea ice in the Bohai Sea.

Digital image correlation based on variable circle template in dual camera matching

In the measurement of the 3-D shape of a large curved object using traditional 3-D-digital image correlation (DIC), the left and right camera images are relatively rotated or deformed too much, which will cause the matching to fail. We propose a 3D-DIC matching method. Compared with traditional methods having fixed-size templates, this method is robust to relative rotation and deformation between dual cameras and is suitable for 3D-DIC measurement of nonplanar objects. In the proposed method, we use polar coordinates to define the matching template as a variable circle, which is convenient for interpolation calculation and changing the template size. The zero-mean normalized cross-correlation method is used to determine the position of an entire pixel on the distorted image. This step can provide an initial value close to the true value for subsequent iterations of the inverse compositional Gauss–Newton algorithm. The performance of the proposed method is verified by experiments. Compared with the traditional matching method, the proposed method is suitable for matching large curved surfaces between 3D-DIC dual cameras and extends the application of DIC technology.

Three-dimensional analysis of the initial stage of convective precipitation using an operational X-band polarimetric radar network

In this study, we propose a new methodology for analysis of the initial stage of localized convective precipitation. The developed algorithm was used to create three-dimensional constant-altitude plan-position-indicator (3D CAPPI) data, which are high-spatiotemporal-resolution volumetric data, using two X-band polarimetric radars that are located in the Kanto region of Japan. Advection vectors are estimated by applying the normalized cross-correlation method to observed successive precipitation echoes on the polar coordinate system at each antenna tilt angle. The estimated advection vectors are expressed by a linear regression model that depends on time and tilt angle. In addition, 3D CAPPI data utilize the mosaic method to obtain further precipitation information from radar observations. The algorithm produces the detailed 3D structure of rapidly developing convective cells and provides quantitative information on convective precipitation, such as the echo top height, maximum reflectivity, and appearance time and height of each cell. The 3D CAPPI mosaic also clearly shows the back-building process at the initial stage of convective precipitation.

3D LiDAR-Based Precision Vehicle Localization with Movable Region Constraints.

This paper discusses a high-performance similarity measurement method based on known map information named the cross mean absolute difference (CMAD) method. Applying the conventional normalized cross-correlation (NCC) feature registration method requires sufficient numbers of feature points, which must also exhibit near-normal distribution. However, Light Detection and Ranging (LiDAR) ranging point cloud data scanned and collected on-site are scarce and do not fulfill near-normal distribution. Consequently, considerable localization errors occur when NCC features are registered with map features. Thus, the CMAD method was proposed to effectively improve the NCC algorithm and localization accuracy. Because uncertainties in localization sensors cause deviations in the localization processes, drivable moving regions (DMRs) were established to restrict the range of location searches, filter out unreasonable trajectories, and improve localization speed and performance. An error comparison was conducted between the localization results of the window-based, DMR–CMAD, and DMR–NCC methods, as well as those of the simultaneous localization and mapping methods. The DMR–CMAD method did not differ considerably from the window-based method in its accuracy: the root mean square error in the indoor experiment was no higher than 10 cm, and that of the outdoor experiment was 10–30 cm. Additionally, the DMR–CMAD method was the least time-consuming of the three methods, and the DMR–NCC generated more localization errors and required more localization time than the other two methods. Finally, the DMR–CMAD algorithm was employed for the successful on-site instant localization of a car.

Hardware Implementation for an Improved Full-Pixel Search Algorithm Based on Normalized Cross Correlation Method

Digital speckle correlation method is widely used in the areas of three-dimensional deformation and morphology measurement. It has the advantages of non-contact, high precision, and strong stability. However, it is very complex to be carried out with low speed software implementation. Here, an improved full pixel search algorithm based on the normalized cross correlation (NCC) method considering hardware implementation is proposed. According to the field programmable gate array (FPGA) simulation results, the speed of hardware design proposed in this paper is 2000 faster than that of software in single point matching, and 600 times faster than software in multi-point matching. The speed of the presented algorithm shows an increasing trend with the increase of the template size when performing multipoint matching.

Anti-occlusion tracking algorithm of video target based on prediction and re-matching strategy

Accurately locating the video target in the process of occlusion and recurrence will be very important for effective follow-up of the target. For the problem of poor applicability of Mean Shift and its improved algorithm when the target is heavily occluded, this paper proposes an anti-occlusion video target tracking algorithm based on prediction and re-matching strategy. Firstly, dynamically combining the Mean Shift algorithm with the Kalman filter, this paper achieves stable tracking of un-occluded target. Secondly, when the target is occluded, Kalman filter is combined with the target prior information to predict the position of the occluded target. Finally, in the recurrence process of occluded targets, the target is re-matched through the normalized cross-correlation method to obtain target optimal position, and then the target can be quickly and accurately located. The simulation results show that the proposed method has strong anti-occlusion and reliability tracking in the video target tracking process.

An Optical Crack Growth Sensor Using the Digital Sampling Moiré Method.

High-accuracy crack growth measurement is crucial for the health assessment of concrete structures. In this work, an optical crack growth sensor using the digital sampling moiré (DSM) method is developed for two-dimensional (2D) crack growth monitoring. The DSM method generates moiré fringes from a single image through digital image processing, and it measures 2D displacements using the phase difference of moiré fringes between motion. Compared with the previous sensors using traditional photogrammetric algorithms such as the normalized cross-correlation (NCC) method, this new DSM-based sensor has several advantages: First, it is of a higher sensitivity and lower computational cost; second, it requires no prior calibration to get accurate 2D displacements which can greatly simplify the practical application for multiple crack monitoring. In addition, it is more robust to the change of imaging distance, which is determined by the height difference between two sides of a concrete crack. These advantages break the limitation of the NCC method and broaden the applicability of the crack growth sensor. These advantages have been verified with one numerical simulation and two laboratory tests.

Comparison of Displacement Tracking Algorithms for in Vivo Electrode Displacement Elastography

Hepatocellular carcinoma and liver metastases are common hepatic malignancies presenting with high mortality rates. Minimally invasive microwave ablation (MWA) yields high success rates similar to surgical resection. However, MWA procedures require accurate image guidance during the procedure and for post-procedure assessments. Ultrasound electrode displacement elastography (EDE) has demonstrated utility for non-ionizing imaging of regions of thermal necrosis created with MWA in the ablation suite. Three strategies for displacement vector tracking and strain tensor estimation, namely coupled subsample displacement estimation (CSDE), a multilevel 2-D normalized cross-correlation method, and quality-guided displacement tracking (QGDT) have previously shown accurate estimations for EDE. This paper reports on a qualitative and quantitative comparison of these three algorithms over 79 patients after an MWA procedure. Qualitatively, CSDE presents sharply delineated, clean ablated regions with low noise except for the distal boundary of the ablated region. Multilevel and QGDT contain more visible noise artifacts, but delineation is seen over the entire ablated region. Quantitative comparison indicates CSDE with more consistent mean and standard deviations of region of interest within the mass of strain tensor magnitudes and higher contrast, while Multilevel and QGDT provide higher CNR. This fact along with highest success rates of 89% and 79% on axial and lateral strain tensor images for visualization of thermal necrosis using the Multilevel approach leads to it being the best choice in a clinical setting. All methods, however, provide consistent and reproducible delineation for EDE in the ablation suite.

Performance Analysis of Vehicle Number Plate Recognition System Using Template Matching Techniques

Vehicle number plate recognition (VNPR) system is a digital image processing techniques which is broadly used in vehicle transportation system to identify the vehicle by their number plate. Yet it’s a very challenging problem, due to the diversity of plate formats, different scales, and non-uniform illumination conditions during image acquisition. This research mainly focuses on Nepali vehicle number plate recognition system in which the vehicle plate image is received by the digital cameras and the image was then processed to obtain the number plate information. A real image of a vehicle is captured and processed using various algorithms. Morphological operations, and edge detection, smoothing, filtering, techniques for plate localization and characters segmentation for segment character and these segmented character was cut in to block of 70×70 size and calculate the correlation with the template of database using the template matching algorithm normalized cross correlation and phase correlation and compare this result in term of accuracy. The system was tested by 90 patterns under several conditions. It includes experiment of number plate recognition using phase correlation and normalized cross correlation methods. From the study and analysis of test after applying on number of images of database, the normalized cross correlation method was found more accurate to recognize the number plate then phase correlation method and recognition accuracy of normalized cross correlation was 67.98% and phase correlation was 63.46%.

An Attitude Jitter Correction Method for Multispectral Parallax Imagery Based on Compressive Sensing

Attitude jitter is a common problem for high-resolution earth-observation satellites and can diminish the geo-positioning and mapping performance of observed images. It is especially necessary to address this problem when high-performance attitude measurements are unavailable. Therefore, an attitude jitter correction method for multispectral parallax imagery that utilizes the compressive-sensing technology is proposed in this letter. In the proposed method, the attitude jitter is estimated from the parallax disparities of different band images, and then the image displacement caused by attitude jitter can be corrected. Using the normalized cross correlation method and compressive-sensing technology, the proposed method can deal with the condition of texture-feature deficiency in the partial image. The multispectral images of the Terra and ZY-3 satellites are used as experimental data to evaluate the proposed method. The registration errors of different bands are greatly reduced in both the cross- and along-track directions, and the experiment results indicate that the proposed method is effective for correcting the attitude jitter of both satellites.

An intelligent computer method for automatic mosaic of sequential slub yarn images based on image processing

In order to analyze the parameters of slub yarn from sequential images accurately, an automatic image mosaic method is proposed in this paper. In this method, a series of overlapping yarn images, which are captured from a moving slub yarn, are stitched into a panorama automatically. Background subtraction, image segmentation and judgment template traversal methods are applied to preprocess the sequential images for obtaining a test image. Subsequently, certain rows in the bottom of the test image are used as a template image to match the next image. The matching coefficient matrix is calculated between the template image and next image based on the normalized cross-correlation method. In the matrix, the coordinates of the peak value are found as the optimal matching points. Two kinds of slub yarn images captured under 40 fps are stitched by using the proposed method and the manual method, respectively. Finally, an objective method is formulated to evaluate the qualities of the image mosaic by the proposed method. The experimental results show that the proposed method can find the match position accurately and is highly consistent with the manual method.

Tracking Dynamic Tongue Motion in Ultrasound Images for Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), a breathing disorder characterized by repetitive collapse of the pharyngeal airway during sleep, can cause intermittent hypoxemia and frequent arousal. The evaluation of dynamic tongue motion not only provides the biomechanics and pathophysiology for OSA diagnosis, but also helps doctors to determine treatment strategies for these patients with OSA. The purpose of this study was to develop and verify a dedicated tracking algorithm, called the modified optical flow (OF)-based method, for monitoring the dynamic motion of the tongue base in ultrasound image sequences derived from controls and patients with OSA. The performance of the proposed method was verified by phantom and synthetic data. A common tracking method, the normalized cross-correlation method, was included for comparison. The efficacy of the algorithms was evaluated by calculating the estimated displacement error. All results indicated that the modified OF-based method exhibited higher accuracy in verification experiments. In the human subject experiment, all participants performed the Müller maneuver (MM) to simulate the contour changes of the tongue base with a negative pharyngeal airway pressure in sleep apnea. Ultrasound image sequences of the tongue were obtained during 10 s of a transition from normal breathing to the MM, and these were measured using the modified OF-based method. The results indicated that the displacement of the tongue base during the MM was larger in the controls than in the patients with OSA (p < 0.05); the calculated areas of the tongue in the controls and patients with OSA were 24.9 ± 3.0 and 27.6 ± 3.3 cm2, respectively, during normal breathing (p < 0.05), and 24.7 ± 3.6 and 27.3 ± 3.8 cm2, respectively, at the end of the MM. The percentage changes in the tongue area were 2.2% and 1.3% in the controls and patients with OSA, respectively. We found that quantitative assessment of tongue motion by ultrasound imaging is suitable for evaluating pharyngeal airway behavior in OSA patients with minimal invasiveness and easy accessibility.