Correlation Template Matching Research Articles

Analysis of centroiding algorithms for non-diffracting structured and hollow structured laser beams.

This paper explores the potential of optical-based systems, specifically pseudo-non-diffractive beams, as an alternative for alignment. The study focuses on structured laser beams and hollow structured laser beams, which exhibit lower divergence and enhanced detection capabilities. The research objective is to analyze and compare centroiding algorithms in terms of accuracy and robustness to noise. The study compares the gamma-corrected and threshold-corrected center of gravity and correlation template matching. It also introduces a polarization-based algorithm.

Structural displacement estimation by a hybrid computer vision approach

Structural displacement is an important indicator of structural safety in structural health monitoring (SHM). As one of the vision-based methods, optical flow can provide displacement measurement of pixels in images. However, estimating structural displacement by predicting the optical flow between the current frame and the initial frame may be subject to limited accuracy due to environmental variations. Alternatively, calculating structural velocity by predicting the optical flow of adjacent frames and integrating to obtain displacement may introduce displacement drift attributable to cumulative errors. This study proposes a hybrid structural displacement estimation method to eliminate the effects of environmental variations and cumulative errors. In comparison to the existing methods, the novelty of the proposed method is to effectively integrate deep learning-based dense optical flow and correlation template matching (CM), for achieving both high accuracy and improved robustness. Deep learning-based dense optical flow was used for optical flow prediction between adjacent frames through correlation calculations and iterative updating to obtain the structural velocity. Pyramid-accelerated CM was employed to locate the regions of interest (ROI) of the structure in each frame, and the structural displacement was then estimated by counting temporal changes in these locations. By fusing estimated structural velocity and displacement using the Kalman filter, optimized structural displacement results were obtained, and temporal cumulative errors using dense optical flow could be eliminated. The proposed method was validated in an indoor shaking table test of a three-story reinforced concrete structure, and an outdoor shaking table test of a cold-formed steel wall system. The results indicated that the proposed method reduced the root mean square error of estimated displacement by over 89% compared with dense optical flow and by over 36% compared with CM. Furthermore, the proposed method was able to process 1080p high-definition images at a rate of 5.43 frames per second, indicating its high efficiency for applications.

Detection and Positioning of Camellia oleifera Fruit Based on LBP Image Texture Matching and Binocular Stereo Vision

To achieve the rapid recognition and accurate picking of Camellia oleifera fruits, a binocular vision system composed of two industrial cameras was used to collect images of Camellia oleifera fruits in natural environments. The YOLOv7 convolutional neural network model was used for iterative training, and the optimal weight model was selected to recognize the images and obtain the anchor frame region of the Camellia oleifera fruits. The local binary pattern (LBP) maps of the anchor frame region were extracted and matched by using the normalized correlation coefficient template matching algorithm to obtain the positions of the center point in the left and right images. The recognition experimental results showed that the accuracy rate, recall rate, mAP and F1 of the model were 97.3%, 97.6%, 97.7% and 97.4%. The recognition rate of the Camellia oleifera fruit with slight shading was 93.13%, and the recognition rate with severe shading was 75.21%. The recognition rate of the Camellia oleifera fruit was 90.64% under sunlight condition, and the recognition rate was 91.34% under shading condition. The orchard experiment results showed that, in the depth range of 400–600 mm, the maximum error value of the binocular stereo vision system in the depth direction was 4.279 mm, and the standard deviation was 1.142 mm. The detection and three-dimensional positioning accuracy of the binocular stereo vision system for Camellia oleifera fruits could basically meet the working requirements of the Camellia oleifera fruit-picking robot.

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

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

Event-based micro vibration measurement using phase correlation template matching with event filter optimization

Condition monitoring of mechanical systems based on vibration is one of the most effective and useful techniques for predictive maintenance. With advancements in image processing and artificial intelligence technologies, vision camera-based condition monitoring has come into the spotlight. An event camera, also called a dynamic vision sensor, operates on a principle different from that of traditional frame-based cameras. However, there is still no standardized method for event camera-based vibration measurement. This study proposes an event filter-based phase correlation template match (EF-PCTM) method, including its optimal design procedure, to measure micro-vibrations using an event camera. In this study, event filter is designed using an infinite impulse response filter and genetic algorithm, and a cost function is defined to improve the performance of EF-PCTM. The performance of the proposed method was verified by two experiments using a rotor system simulator: i) extraction of micro-vibration and ii) fault condition monitoring. The proposed image processing procedure using EF-PCTM makes it possible to measure micro-vibrations, which had been challenging to measure in the past, and calculate the operational deflection shape.

A Method of Improving the Length Measurement Accuracy of Metal Parts Using Polarization Vision

Measurement technology based on machine vision has been widely used in various industries. The development of vision measurement technology mainly depends on the process of photosensitive components and the algorithm of processing a target image. In the high-precision dimension measurement of machined metal parts, the high-resolution imaging device usually exposes the cutting texture of the metal surface and affects the accuracy of measurement algorithm. At the same time, the edges of machined metal parts are often chamfered, which makes the edges of objects in the picture overexposed in the lighting measurement environment. These factors reduce the accuracy of dimensioning metal parts using visual measurements. The traditional vision measurement method based on color/gray image makes it difficult to analyze the physical quantities in the light field except for the light intensity, which limits the measurement accuracy. Polarization information can more carefully depict the edge contour edge information in the scene and increase the contrast between the foreground and the background. This paper presents a method to improve the measurement accuracy of machined metal parts by using polarization vision. The incident angle of the light source is optimized according to the complex refractive index of the metal material, and the degree of polarization image with enhanced edge contour features of the ROI (region of interest) is obtained. The high-precision measurement of cylindrical brass motor components is realized by using the method of reprojection transformation correction and maximum correlation template matching (NCC) for rough positioning, as well as the method of edge extraction and optimal fitting. The experimental results show that for copper parts with a tolerance range of ±0.1 mm, the average measurement error and maximum measurement error are within 0.01 mm, which are higher than the existing color/gray image measurement methods.

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.

Comparison of Spectral and Template Matching Features for SSVEP BCI Target Frequency Classification

Brain-computer interfaces (BCI) provide new communication and control channels to restore and support these functions of the restricted users. Among these Visual Evoked Potential (VEP) based BCIs are the most promising in terms of ease of use and performance. The frequency following phenomenon of VEPs produce Steady State Visual Evoked Potentials (SSVEP) at the frequency of stimulation of the human visual system. In such interface systems, each target is encoded with a particular stimulation frequency and phase. In communication purpose speller interfaces each target flickers a letter or character with the stimulation frequency and phase. The detection of the focused target correctly by the computer is required. In this process classification methods and feature extraction method play critical roles. This study used a publicly available benchmark dataset of a 40 target SSVEP BCI. In the analysis, two feature vectors are obtained from power spectrum parameters and one from stimulus template matching correlation coefficients. The performance of the three classification methods, namely Fine Tree, Linear Discriminant Analysis (LDA) and K-Nearest Neighbors (KNN), are compared using these feature vectors. Spectral features performed better than the template matching features. Especially target frequency signal ratio (TFSR) to the total stimulation band energy features provided better accuracy values. LDA and KNN performed better than decision tree in classification.

Japanese Hiragana Handwriting Pattern Recognition Using Template Matching Correlation Method

Hiragana is one of the traditional Japanese letters used to translate native Japanese words. The introduction of an object requires a learning process, which is obtained through the characteristic in the form of unique features on similar objects, but manually it is quite difficult to distinguish these letters. This writing explains the discussion system to differentiate between hiragana letters starting from preprocess namely grayscale and threshold, then segmenting and normalization, while image classification uses the Template Matching Correlation method. The results of tests carried out assessing the test rate of around 76% using the Matching Template Correlation method. While the remaining 14% indicates that the object identified does not match the intended results.

Research on Method of Farmland Obstacle Boundary Extraction in UAV Remote Sensing Images.

Aimed at the problem of obstacle detection in farmland, the research proposed to adopt the method of farmland information acquisition based on unmanned aerial vehicle landmark image, and improved the method of extracting obstacle boundary based on standard correlation coefficient template matching and assessed the influence of different image resolutions on the precision of obstacle extraction. Analyzing the RGB image of farmland acquired by unmanned aerial vehicle remote sensing technology, this research got the following results. Firstly, we applied a method automatically registering coordinates, and the average deviations on the X and Y direction were 4.6 cm and 12.0 cm respectively, while the average deviations manually by ArcGIS were 4.6 cm and 5.7 cm. Secondly, with an improvement on the step of the traditional correlation coefficient template matching, we reduced the time of template matching from 12.2 s to 4.6 s. The average deviation between edge length of obstacles calculated by corner points extracted by the algorithm and that by actual measurement was 4.0 cm. Lastly, by compressing the original image on a different ratio, when the pixel reached 735 × 2174 (the image resolution reached 6 cm), the obstacle boundary was extracted based on correlation coefficient template matching, the average deviations of boundary points I of six obstacles on the X and Y were respectively 0.87 and 0.95 cm, and the whole process of detection took about 3.1 s. To sum up, it can be concluded that the algorithm of automatically registered coordinates and of automatically extracted obstacle boundary, which were designed in this research, can be applied to the establishment of a basic information collection system for navigation in future study. The best image pixel of obstacle boundary detection proposed after integrating the detection precision and detection time can be the theoretical basis for deciding the unmanned aerial vehicle remote sensing image resolution.

SMART tracking: Simultaneous anatomical imaging and real-time passive device tracking for MR-guided interventions

PurposeThis study demonstrates a proof of concept of a method for simultaneous anatomical imaging and real-time (SMART) passive device tracking for MR-guided interventions. MethodsPhase Correlation template matching was combined with a fast undersampled radial multi-echo acquisition using the white marker phenomenon after the first echo. In this way, the first echo provides anatomical contrast, whereas the other echoes provide white marker contrast to allow accurate device localization using fast simulations and template matching. This approach was tested on tracking of five 0.5 mm steel markers in an agarose phantom and on insertion of an MRI-compatible 20 Gauge titanium needle in ex vivo porcine tissue. The locations of the steel markers were quantitatively compared to the marker locations as found on a CT scan of the same phantom. ResultsThe average pairwise error between the MRI and CT locations was 0.30 mm for tracking of stationary steel spheres and 0.29 mm during motion. Qualitative evaluation of the tracking of needle insertions showed that tracked positions were stable throughout needle insertion and retraction. ConclusionThe proposed SMART tracking method provided accurate passive tracking of devices at high framerates, inclusion of real-time anatomical scanning, and the capability of automatic slice positioning. Furthermore, the method does not require specialized hardware and could therefore be applied to track any rigid metal device that causes appreciable magnetic field distortions.

MP01-07 USE OF COMPUTER VISION MOTION ANALYSIS TO AID IN SURGICAL SKILL ASSESSMENT OF SUTURING TASKS

You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment I1 Apr 2018MP01-07 USE OF COMPUTER VISION MOTION ANALYSIS TO AID IN SURGICAL SKILL ASSESSMENT OF SUTURING TASKS Brady Miller, David Azari, Robert Radwin, and Brian Le Brady MillerBrady Miller More articles by this author , David AzariDavid Azari More articles by this author , Robert RadwinRobert Radwin More articles by this author , and Brian LeBrian Le More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.113AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Marker-less video motion analysis can quantify kinematic properties of simulated operative tasks without need for expert review which can be time-consuming and subjective. Here, we assess the feasibility of this technology in the surgical simulation setting and compare hand summary kinematics with a standardized self-assessment using two simulated tasks. METHODS Medical students, residents, attending and retired surgeons completed simulated simple interrupted and running subcuticular suturing tasks. Performance was self-rated using previously tested visual analog motion scales. Digital cameras were positioned to record hand motions at 30 frames/sec without markers. Video analysis utilized a semi-supervised cross correlation template matching algorithm, producing an x-y pixel location of the participant’s dominant hand across successive video frames, enabling the kinematic parameters speed, acceleration and jerk to be calculated. RESULTS Participants (n=35) were recorded performing both tasks (n=70 observations), totaling 5.2 hours (avg 4:45 min/video) video time. The two most common sources of video artifact requiring analyst adjustment were (1) drift of hands outside the frame and (2) transient obscuring of the frame by participant's head. The time required to supervise the tracking algorithm varied greatly, ranging from 3 to 10 times the original segment length. Despite some tracking artifact, kinematic parameters were calculated for all (100%) observations. Mean acceleration was greater for residents (631.1 mm/sec^2) than attendings (577.8), students (563.9) and retirees (471.2), though was not statistically different (p=0.32). Mean speed and jerk index were also greater for residents than others but also not statistically different (p=0.27). Self-coordination rating was weakly correlated with speed (r=0.24, p=0.04), acceleration (r=0.31, p=0.01) and jerk (r=0.31, p=0.01). CONCLUSIONS Marker-less video motion analysis successfully tracked hand movement and represents a potentially high-throughput tool with on-demand availability to provide objective feedback. Some difference by experience level was suggested that may be accentuated with increasingly difficult tasks. Future work should focus reducing need for manual adjustment. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e4 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Brady Miller More articles by this author David Azari More articles by this author Robert Radwin More articles by this author Brian Le More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Implementasi Aplikasi Hijab Berbasis Android dengan Metode Canny Operator dan Template Matching Correlation

Abstrak --- Hijab is a head covering for Muslim women. Hijab can be developed as an application that is implemented based on android. Applications hijab are scattered on android, but the application hijab which exist at present more an assortment of hijab models only. It is considered less supporting the needs of women who hijab. Because the woman was kept at this time included more selective in choosing the model that modern hijab, but in accordance with the Islamic Shari'a. Of these problems led to the idea to create applications based on android hijab, in which modern hijab model provides the appropriate guidance of Islam and to provide information based on the shape of the face veil user at the first capture the face. The methodology used in the making of this application is a method Prototype. The programming language is JAVA (Eclipse). While the method for classification process that distinguishes types of faces in this study using two methods Canny operator and Template Matching Correlation. On the Application hijab, Input as the image of the user's face to be conducted face detection process and display the results of a user's face detection process. Then display the corresponding information hijab Islamic guidance is also based on the shape of the face of the user face detection process. Face shapes used are rounded, long, oval and square with the characteristic that distinguishes every face shape as a reference. While the basics of hadith and verses in the Qur'an make reference to the appropriate information hjab Islamic guidance. With the Android-Based Application hijab is expected of the veiled women to know and implement the hijab correctly. Keywords --- Hijab, Java (Eclipse), Canny operator, Template Matching Correlation.

Iraqi Cars License Plate Detection and Recognition System using Edge Detection and Template Matching Correlation

Automatic License Plate Detection and Recognition (ALPDR) system is an image processing technology used to identify cars by their license plates.This system consists of two main parts: the first is a practical implementation of how to take an automatically snapshotfor cars passes, this was done by two sensors type(GP2Y0A21YK0F sensor) connected with each other and interfaced with a A4tech USB camera,and this camera is interfaced using Matlab 2013a with the personal computer. The second part is image processing, this part includes four basic stages: The first stage is image preprocessing which involves image normalization and RGB to gray image conversion. Second stage is detection of a possible license plate using edge detection technique and extracts these LP using region growing technique, detection rates reach to 95%. Third stage is alphanumeric character segmentation to isolate each character, numbers and words of the license plate using Otsu's and Hough transforms technique for subsequent recognition. The last stage reads the alphanumeric character and words by correlation template matching, which is a simple, fast and given a recognition rate reach to 98.245%.

OPTICAL CHARACTER RECOGNITION MENGGUNAKAN ALGORITMA TEMPLATE MATCHING CORRELATION

OCR (Optical Character Recognition) is an effective solution to the process of converting printed documents into digital documents. The problems that arise in the process of computer letters recognition is how a recognition techniques to identify different types of characters with different sizes and shapes. Recognition method used in this final project is the template matching correlation method. Prior to the recognition process, the input image with a format *.bmp or *.jpg processed first at the preprocessing process, which includes the binerisasi, segmentation, and normalization of images. Average recognition success rate of 92,90% is generated by this system. The final results showed that the use of the template matching correlation method is effective enough to build an OCR system with good accuracy.

Magnetic Resonance Imaging Assessment of Spinal Cord and Cauda Equina Motion in Supine Patients With Spinal Metastases Planned for Spine Stereotactic Body Radiation Therapy

To assess motion of the spinal cord and cauda equina, which are critical neural tissues (CNT), which is important when evaluating the planning organ-at-risk margin required for stereotactic body radiation therapy. We analyzed CNT motion in 65 patients with spinal metastases (11 cervical, 39 thoracic, and 24 lumbar spinal segments) in the supine position using dynamic axial and sagittal magnetic resonance imaging (dMRI, 3T Verio, Siemens) over a 137-second interval. Motion was segregated according to physiologic cardiorespiratory oscillatory motion (characterized by the average root mean square deviation) and random bulk shifts associated with gross patient motion (characterized by the range). Displacement was evaluated in the anteroposterior (AP), lateral (LR), and superior-inferior (SI) directions by use of a correlation coefficient template matching algorithm, with quantification of random motion measure error over 3 separate trials. Statistical significance was defined according to P<.05. In the AP, LR, and SI directions, significant oscillatory motion was observed in 39.2%, 35.1%, and 10.8% of spinal segments, respectively, and significant bulk motions in all cases. The median oscillatory CNT motions in the AP, LR, and SI directions were 0.16mm, 0.17mm, and 0.44mm, respectively, and the maximal statistically significant oscillatory motions were 0.39mm, 0.41mm, and 0.77mm, respectively. The median bulk displacements in the AP, LR, and SI directions were 0.51mm, 0.59mm, and 0.66mm, and the maximal statistically significant displacements were 2.21mm, 2.87mm, and 3.90mm, respectively. In the AP, LR, and SI directions, bulk displacements were greater than 1.5mm in 5.4%, 9.0%, and 14.9% of spinal segments, respectively. No significant differences in axial motion were observed according to cord level or cauda equina. Oscillatory CNT motion was observed to be relatively minor. Our results support the importance of controlling bulk patient motion and the practice of applying a planning organ-at-risk margin.

一种基于Kalman滤波的环形模板匹配相关跟踪算法

The global searching method is adopted in the traditional correlation algorithms in target tracking system, resulting in the large amounts of calculation, the difficulty of real-time implementation and even the loss of the tracking target when the target is partially shaded or in sharp attitude variation. To solve the problems, an improved approach based on the loop correlation template matching and Kalman filter is proposed to realize target tracking. Kalman filter, to make the tracking more active, estimates the possible region of the target in the next frame at first,and finds the best matching points by the loop correlation matching in the aforementioned region while the loop-template matching overcomes the loss of the horizontal matching points caused by the sharp attitude variation. So the algorithm has strong robustness, and is of translation and rotation invariance. In the experiments, the proposed algorithm is applied to the tracking of a main battle tank partially shaded and a jet with Viktor Pugachev's Cobra . Compared with the traditional algorithms, the algorithm herein can steadily track the targets in less time without losing them.

High-speed Image Matching for Printed Circuit Board Using Edge Direction Histogram by All Pixels

Normalized correlation template matching is applied to industrial image processing fields. However a target image area must be searched has the right potion and the same scale as a registered template image. This paper proposed a method to search a target image area that has free location and inclination in a gray-scaled image of printed circuit boards. Because electrical components and wires are set vertically and horizontally on a printed board, it is likely that edge directions are distributed vertically and horizontally a lot. The proposed method can estimate the inclination of a target image area by feature of edge directions and can reduce the computational cost for template matching. The inclination is measured by the edge direction histogram based on edge strength. The experimental results show that the inclinations of target image areas were measured with the averaged accurate of ± 2 degree and the computational cost was reduced to about 1% in comparison with the conventional method.