Normalized Cross Correlation Algorithm Research Articles

A Robust Tie-Points Matching Method with Regional Feature Representation for Synthetic Aperture Radar Images

The precise tie-points (TPs) on synthetic aperture radar (SAR) images are a critical cornerstone in the global digital elevation model (DEM) and digital ortho map (DOM) production process. While there are abundant studies on SAR TPs matching, improvement opportunities persist in large areas. The correspondences have pixel-level errors during geocoding, which result in misalignment between global products. Consequently, this paper proposed a robust method for SAR images TPs matching, which consists of three key steps: (1) interest point extraction based on the dynamic Harris area entropy (DHAE) grid; (2) adaptive determination of template size; (3) normalized cross correlation (NCC) template matching. DHAE is a regional texture information grid based on the SAR-Harris map, and it is achieved through dynamic block division. Generating the DHAE grid over SAR images enables the extraction of interest points that have regional feature representation and distribution uniformity. A variable-size matching template is adaptively determined based on DHAE to enhance template quality while maintaining computational efficiency. Subsequently, the NCC algorithm is employed to find subpixel-precise correspondences. The proposed method is applied on TPs matching in 57 Terra-SAR images, which cover a large geographical area. Furthermore, the overlapping area is partitioned into five segments according to different coverage types. The experimental results demonstrate that the proposed method outperforms other template matching methods. For all coverage types, the proposed method exhibits high-precision sub-pixel results that reach up to 38.64% in terms of the relative positioning error (RPE), particularly in texture-weak and large areas.

A novel approach to feasible optimal cooperative search and coverage for wildfire emergency management

This paper presents a novel near non-dominated solution approach for distributed cooperative search and coverage (CSC) in uncertain environments for wildfire emergency management. The goal is to efficiently cover the extent of the wildfire and search for unknown stationary fire hotspots using a team of flying agents. Certain management factors such as time and the number of agents can directly impact the effectiveness of cooperative wildfire coverage and fire hotspots search in environments with scattered and widespread uncertainties. To address this, a near non-dominated solutions approach for a new CSC mission in a specific forest region is proposed in this paper. This approach involves generating a dataset based on previously completed wildfire management missions and probability features matching processes. Accordingly, prime and appointed surveillance regions are defined, and previously completed CSC missions are formulated as optimization problems to generate an optimal dataset. Afterward, the normalized cross-correlation algorithm is utilized to identify data that closely matches the probability pattern of the initial probability map of the current mission. By allocating the parameters of the identified data to the operational parameters of the current mission, the performance of the distributed cooperative fire hotspot search and wildfire coverage mission can be enhanced. This allocated data to the current mission serves as the near non-dominated solution. The effectiveness of the proposed strategy is validated and demonstrated via CSC simulations.

Accuracy Analysis for Shack–Hartmann Wave Front Sensing with Extended Sources

Correlating the Shack–Hartmann wave front sensor (SH-WFS) with extended targets is widely used in solar adaptive optics systems. This paper aims to introduce a theoretical analysis that evaluates the accuracy of the SH-WFS on extended sources, with a specific focus on the implementation of the Normalized Cross-correlation (NCC) algorithm. To obtain an accurate error description, we utilized the calculation formula of the NCC algorithm to directly express the coordinates of the maximum value in the correlation function matrix. Furthermore, we determined the variance of the centroid position through the error transfer function, which quantifies the measurement error. In comparison with the previous findings of Michau et al., our result exhibits a coefficient disparity, specifically obtaining results 1.5 times higher than their work. The extensive solar granulation simulation and experimental results validate the theoretical error formulas. These error formulas can effectively estimate the accuracy of the SH-WFS, providing a theoretical foundation for the design of optical systems.

Real-Time Vehicle Identification for Improving the Traffic Management system-A Review

Due to the increasing number of cars on the road and the exponential growth of traffic throughout the globe, regulating traffic has become crucial in the most industrialized countries. The development of technology has led to the current state of traffic management systems that comes with the ability to count, monitor, and predict the speed of vehicles in order to improve the transportation planning. This has also reduced the number of accidents that occur due to worsen traffic conditions. Road traffic surveys have been carried out manually for a long time since automated measures were not often employed due to the difficulty of installation. Machine learning in image processing is widely recognized as a significant approach for real-world applications such as traffic monitoring. The primary benefit of automated vehicle counting is that it allows for the management and evaluation of traffic in the urban transportation system. There are many methods employing distributed acoustic systems on intelligent transportation systems, including YOLO v4 and the Normalized Cross-correlation algorithm, which uses ultrasonic sensors and the algorithms ALPR, YOLO, GDPR, and CNN. The simplest method for identifying a vehicle is to gather information from sensors such as cameras, vibration detectors, ultrasound detectors, or acoustic detectors. These sensors are combined with the proper microcontrollers to determine the amount of traffic using the most recent data and theory. This review article is a quick reference for researchers working on safety-related traffic management systems.

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.

Search for Lensing Signatures from the Latest Fast Radio Burst Observations and Constraints on the Abundance of Primordial Black Holes

Abstract The possibility that primordial black holes (PBHs) form some part of dark matter has been considered for a long time but poorly constrained over a wide mass range. Fast radio bursts (FRBs) are bright radio transients with millisecond duration. Their lensing effect has been proposed to be one of the cleanest probes for constraining the presence of PBHs in the stellar-mass window. In this paper, we first apply the normalized cross-correlation algorithm to search and identify candidates for lensed FRBs in the latest public FRB observations, i.e., 593 FRBs, which mainly consist of the first Canadian Hydrogen Intensity Mapping Experiment FRB catalog, and then derive constraints on the abundance of PBHs from the null search result of the lensing signature. For a monochromatic mass distribution, the fraction of dark matter made up of PBHs could be constrained to ≤87% for ≥500 M ⊙ at the 95% confidence level by assuming signal-to-noise ratios dependent on the flux ratio threshold for each FRB and that apparently one-off events are intrinsic single bursts. This result would be improved by a factor of 3 when a conventional constant flux ratio threshold is considered. Moreover, we derive constraints on PBHs with a log-normal mass function naturally predicted by some popular inflation models and often investigated with gravitational-wave detections. We find that, in this mass distribution scenario, the constraint from the current public FRB observations is relatively weaker than the one from gravitational-wave detections. It is foreseen that upcoming complementary multimessenger observations will yield considerable constraints on the possibilities of PBHs in this intriguing mass window.

2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation

Image-guided surgery (IGS) can reduce the risk of tissue damage and improve the accuracy and targeting of lesions by increasing the surgery’s visual field. Three-dimensional (3D) medical images can provide spatial location information to determine the location of lesions and plan the operation process. For real-time tracking and adjusting the spatial position of surgical instruments, two-dimensional (2D) images provide real-time intraoperative information. In this experiment, 2D/3D medical image registration algorithm based on the gray level is studied, and the registration based on normalized cross-correlation is realized. The Gaussian Laplacian second-order differential operator is introduced as a new similarity measure to increase edge information and internal detail information to solve single information and small convergence regions of the normalized cross-correlation algorithm. The multiresolution strategy improves the registration accuracy and efficiency to solve the low efficiency of the normalized cross-correlation algorithm.

Application of Image Mosaic Technology in Tai Chi Animation Creation.

Panorama can reflect the image seen at any angle of view at a certain point of view. How to improve the quality of panorama stitching and use it as a data foundation in the “smart tourism” system has become a research hotspot in recent years. Image stitching means to use the overlapping area between the images to be stitched for registration and fusion to generate a new image with a wider viewing angle. This article takes the production of “Tai Chi” animation as an example to apply image stitching technology to the production of realistic 3D model textures to simplify the production of animation textures. A handheld camera is used to collect images in a certain overlapping area. After cylindrical projection, the Harris algorithm based on scale space is adopted to detect image feature points, the two-way normalized cross-correlation algorithm matches the feature points, and the algorithm to extract the threshold T iteratively removes mismatches. The transformation parameter model is quickly estimated through the improved RANSAC algorithm, and the spliced image is projected and transformed. The Szeliski grayscale fusion method directly calculates the grayscale average of the matching points to fuse the image, and finally, the best stitching method is used to eliminate the ghosting at the image mosaic. Data experiments based on Matlab show that the proposed image splicing technology has the advantages of high efficiency and clear spliced images and a more satisfactory panoramic image visual effect can be achieved.

Identification of Corrosion Minerals Using Shortwave Infrared Hyperspectral Imaging.

In this study, we propose a new method to identify corrosion minerals in carbon steel using hyperspectral imaging (HSI) in the shortwave infrared range (900–1700 nm). Seven samples were artificially corroded using a neutral salt spray test and examined using a hyperspectral camera. A normalized cross-correlation algorithm is used to identify four different corrosion minerals (goethite, magnetite, lepidocrocite and hematite), using reference spectra. A Fourier Transform Infrared spectrometer (FTIR) analysis of the scraped corrosion powders was used as a ground truth to validate the results obtained by the hyperspectral camera. This comparison shows that the HSI technique effectively detects the dominant mineral present in the samples. In addition, HSI can also accurately predict the changes in mineral composition that occur over time.

A Fast Template Matching Scheme of Visible and Infrared Image Under Occluded Scenarios

A fast and robust template matching scheme, called Matching by Slice Transform Matrix Mapping (MSTMM), is proposed for the matching difficulty under occluded scenarios caused by nonlinear intensity differences and structure differences between visible and thermal infrared images. The first step in the MSTMM scheme was to extract information about the distribution of pixels with the same gray level through the developed Expanded Slice Transform with Adaptive Gray Level (EST-AGL). After completing the construction of the EST-AGL matrix for all image patches, different EST-AGL matrices were mapped to different integers or floats through the traditional special integer mapping mechanism or the neural network mapping mechanism. Finally, template matching between visible and thermal infrared images was achieved by evaluating the similarity of correlation mapping surface images through the Normalized Cross Correlation (NCC) algorithm. The proposed EST-AGL method can overcome the nonlinear intensity differences between visible and thermal infrared images by extracting the structural features of the image. The mapping mechanism of the MSTMM scheme can reduce the structural differences between the normal template image and the query image under an occluded scenario by increasing the similarity between the normal image patches and the image patches with occlusion. The proper mapping mechanism ensures the high performance of the MSTMM scheme by using only the simple NCC algorithm instead of other time-consuming anti-occlusion dense feature algorithms in the similarity evaluation stage. The three main experimental results of the MSTMM scheme are as follows: (1) the scheme of MSTMM can achieve template matching in only 0.015 seconds when a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$64\times 64$ </tex-math></inline-formula> template image slides on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$256\times 256$ </tex-math></inline-formula> query image on a hardware platform with limited resources; (2) the matching success rate of the MSTMM scheme can reach up to 75% among 2107 experimental samples; and (3) the neural network training in the neural network mapping mechanism only takes at least 104.4 seconds on the CPU.

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.

A Novel Normalized Cross-Correlation Speckle-Tracking Ultrasound Algorithm for the Evaluation of Diaphragm Deformation.

Objectives: To develop a two-dimensional normalized cross-correlation (NCC)-based ultrasonic speckle-tracking algorithm for right diaphragm deformation analysis.Methods: Six healthy and eight mechanical ventilation patients were enrolled in this study. Images were acquired by a portable ultrasound system in three sections. DICOM data were processed with NCC to obtain the interframe/cumulative vertical and horizontal displacements, as well as the global strain of the right diaphragm, with continuous tracking and drift correction.Results: The NCC algorithm can track the contraction and relaxation of the right diaphragm by following the respiratory movement continuously. For all three sections, the interframe and accumulated horizontal displacements were both significantly larger than the corresponding vertical displacements (interframe p values: 0.031, 0.004, and 0.000; cumulative p values: 0.039, 0.001, and <0.0001). For the global strain of the right diaphragm, there was no significant difference between each pair of sections (all p > 0.05), regardless of whether the horizontal interval of the initial diaphragm point was 1, 3, 5, or 10 times in the sampling interval.Conclusions: This study developed a novel diaphragm deformation ultrasound imaging method. This method can be used to estimate the diaphragm interframe/accumulated displacement in the horizontal and vertical directions and the global strain on three different imaging planes, and it was found that the strain was not sensitive to the imaging plane.

Retinal cross-section motion correction in three-dimensional retinal optical coherence tomography.

Motion correction is an important issue in ophthalmic optical coherence tomography (OCT), and can improve the ability of data sets to reflect the physiological structures of tissues and make visualization and subsequent analysis easier. In this study, we present a novel method to correct the cross-sectional motion artifacts in retinal OCT volumes. Motion along the x-direction (fast-scan direction) is corrected through the normalized cross-correlation algorithm, while axial motion compensation is performed using the polynomial fitting method on the inner segment/outer segment (IS/OS) layer segmented by the shortest path faster algorithm (SPFA). The results of volunteers with central serous chorioretinopathy demonstrate that the proposed method effectively corrects motion artifacts in OCT volumes and may have potential application value in the evaluation of ophthalmic diseases such as diabetic retinopathy, glaucoma and age-related macular degeneration.

Target tracking algorithm combined part-based and redetection for UAV

In the process of target tracking for UAV video images, the performance of the tracking algorithm declines or even the tracking fails due to target occlusion and scale variation. This paper proposes an improved target tracking algorithm based on the analysis of the tracking framework of the kernel correlation filter. First, four subblocks around the center of the target center are divided. A correlation filter fusing Histogram of Oriented Gradient (HOG) feature and Color Name (CN) feature tracks separately each target subblocks. According to the spatial structure characteristics in the subblocks, the center location and scale of the target are estimated. Secondly, the correct center location of target is determined by the global filter. Then, a tracking fault detection method is proposed. When tracking fails, the target redetection module which uses the normalized cross-correlation algorithm (NCC) to obtain the candidate target set in the re-detection area is started. Besides, this algorithm uses the global filter to obtain real target from the candidate set. In the meanwhile, this algorithm adjusts sectionally the learning rate of the classifiers according to detection results. Lastly, the performance of this algorithm is verified on the UAV123 dataset. The results show that compared with several mainstream methods, that of this algorithm is significantly improved when dealing with target scale variation and occlusion.

Monitoring of Volume of Air in Inhalation From Triflo Using Video Processing

Breathing exercise training with a flow-oriented incentive spirometer (or Triflo) is important in lung expansion rehabilitation for patients with lung surgery or pulmonary injury. In this article, we propose a novel technique to estimate the volume of air inhaled and the sustained inhalation time based on video processing. The proposed technique combines a randomized circle detection algorithm to detect the three balls in Triflo tubes and a normalized cross-correlation algorithm to track these three balls that rise due to inhalation. The values of inhalation and the sustained inhalation time intervals are automatically recorded as screenshots on a personal computer for later analysis by a doctor. Three major experiments were conducted to verify the advantages of this proposed system. The first experiment evaluated the performances of both the ball detection and the tracking algorithms showing 3.68% error. The second experiment applied the proposed method implemented, so that it displays in plots the changes in the inhalation rate, as the training of a postoperative volunteer progressed. In the last experiment, a healthy subject employs Triflo to test the inhalation training format for lung volume regeneration with three levels of difficulty, to record the number of successful breathing maneuvers. When the data are displayed in rate versus time plots, a graph can help a doctor to appropriately plan the training program for the benefit of patients, because a more adaptable therapy can be realized and varying degrees of difficulty can be implemented. In addition, the patients can comfortably practice in home-based rehabilitation, seeing the improvements in the inspiratory muscle performance in computer screenshots.

Calibrating a camera focused on a long shot using a calibration plate and defocused corner points

When focusing on a long shot, it is very difficult to calibrate a camera with high precision. We propose a method of obtaining a close shot of the calibration plate, extracting corner points from the defocused image of the calibration plate, and constructing a three-dimensional control point to calibrate the camera. We construct a defocused corner template and extract the corner points from the defocused image of the calibration plate using the normalized cross-correlation algorithm. Experimental results show that, after the camera has been calibrated by this method, the average pixel distance accuracy of the re-projected detection point pixel at 40 m reaches 1.9 pixels. The average spatial distance accuracy of the corresponding three-dimensional point reaches 2.1 mm.

Accuracy Improvement of Water Level Measuring Method Using Stereo CCTV System

최근 농업용 중소규모 저수지는 노후화 및 안전성 저하 등으로 국지성 호우 발생 시 붕괴사고와 같은 문제가 발생되고 있다. 본 연구는 입체형 CCTV를 이용하여 입체영상을 획득하고, 자동으로 수위 변화를 측정할 수 있는 시스템을 개발하여 재해에 취약한 중소저수지를 효율적으로 관리하고자 한다. 이를 위하여 시스템 개발은 UMI(unified modeling language)를 이용하여 시스템을 개발하였고, 시스템 구현은 CCTV 검정, 취득한 영상의 특징점 및 공액점을 독취 후 에피폴라 기하관계 구현, 표준 상호 상관성(nomalized cross-correlation) 알고리즘을 이용한 영상정합 등을 통하여 시차맵를 제작하여 수위 변화를 분석하였다. 그 결과 본 연구에서 개발한 입체형 CCTV 자동수위측정시스템의 정확도가 기존 시스템에 비하여 좋은 결과를 얻을 수 있었다.Recently, problems have arisen in small and medium-sized reservoirs used for agriculture, such as cave-in incidents during local heavy rainfalls, owing to its deterioration and safety degradation. In this study, we acquired stereo images using stereo closed-circuit television (CCTV) and developed an automatic system that can measure water level changes in order to efficiently manage small and medium-sized reservoirs that are vulnerable to disasters. The system was developed using the unified modeling language (UML) and implemented by testing the CCTV. Epipolar geometry was utilized after reading the key points and conjugate points of the acquired images. The water level changes were then analyzed by producing a disparity map through the image matching using a normalized cross-correlation algorithm. The stereo CCTV-based automatic water level measurement system developed in this study exhibited a high accuracy compared to the conventional system.

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.

Normalized cross correlation image stitching algorithm based on minimum spanning tree

This paper presents a normalized cross correlation optimization algorithm based on minimum spanning tree. This method smoothes the original image and generates normalized correlation matrix for the smoothing image. According to the generated normalized cross-correlation matrix, the maximum value and corresponding index of each row and column (one point in each image relative to all corresponding points in another image) are obtained; according to the result, feature points are matched, and the minimum spanning tree is used in the matching process to plan the fastest traversal route. Two groups of pictures are used to calculate the feature point detection time, the matching time of feature points and the total matching time. Experiments show that the improved algorithm can save 2.72s and 0.53s, 2.68s and 0.56s compared with the traditional normalized cross-correlation algorithm and the optimized cross-correlation algorithm without minimum spanning tree. Experiments show that the proposed algorithm has higher accuracy and convenience, cost-effective and easy to implement.

Automated vision positioning system for dicing semiconductor chips using improved template matching method

This study proposes an automated vision positioning system to realize high-efficient and high-precision positioning and dicing of semiconductor chips in an automatic dicing saw. In this method, image pyramid construction was established to improve the searching speed of feature images by using the pyramid hierarchical search strategy. Hough transformation was used to obtain the approximate angle of the feature images of the semiconductor chips. The improved template matching approach based on the initial angle was proposed to rapidly calculate rotation angle and feature position. Polynomial fitting was adopted to achieve sub-pixel positioning accuracy. Experimental results showed that the proposed algorithm can realize high-precision and real-time recognition under the weak light, strong light, uneven illumination, and rotation angle. The success rate is 99.25%, and the time consumed is only 1/4 of the normalized cross-correlation algorithm. The vision positioning and dicing experiment of the semiconductor chips was carried out on a high-precision dicing saw. The results confirm that the improved algorithm could be used for high-precision and real-time dicing semiconductor chips.