Template Matching Algorithm Research Articles

A fast template matching algorithm based on principal orientation difference

In the case that the background scene is dense map regularization complex and the detected objects are low texture, the method of matching according to the feature points is not applicable. Usually, the template matching method is used. When training samples are insufficient, the template matching method gets a worse detection result. In order to resolve the problem stably in real time, we propose a fast template matching algorithm based on the principal orientation difference feature. The algorithm firstly obtains the edge direction information by comparing the images that are binary. Then, the template area is divided where the different features are extracted. Finally, the matching positions are searched around the template. Experiments on the videos whose speed is 30 frames/s show that our algorithm detects the low-texture objects in real time with a matching rate of 95%. Compared with other state-of-art methods, our proposed method reduces the training samples significantly and is more robust to the illumination changes.

Angle and Scale Invariant Template Matching for Handling Image Distortions

Template matching is a diagnostic approach for detecting a patch of a template image in a given source image. This plays a vital role in multitudinal computer vision applications. In this paper, we propose a methodology that makes the naive template matching algorithm scale and angle invariant during the image recognition process where the source and template is converted to gray scale which makes the technique enhance its proficiency. The proposed algorithm handles the arbitrary modulations of the image patch with respect to size and angle by an exhaustive search of all combinations of sizes are done along with populous combinations of angles. The images adapted are subjected to certain filtering and convolution methods which deepens the quality of the images which in turn assists in retrieving the features with accuracy. The image intensities are adjusted using histogram equalization to enhance the image contrast. These images are then subjected to perform template matching using normalized cross correlation to measure similarity between those two images.

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 ...

Estimation of Mango Tree Count and Crown Cover Delineation using Template Matching Algorithm

Estimation of Mango Tree Count and Crown Cover Delineation using Template Matching Algorithm

Robust vision sensor for multi-point displacement monitoring of bridges in the field

Computer vision sensors have great potential for accurate remote displacement monitoring in the field. This paper presents InnoVision, a video image processing technique developed by the authors to address a number of difficulties associated with the application of the vision sensors to monitoring structural displacement responses in the outdoor condition that are rarely comprehensively studied in literatures. First, limited lighting condition in the field presents a challenge to tracking low contrast features on the structural surface using intensity-based template matching algorithms. For tackling this challenge, a gradient based template matching algorithm is formulated. Second, to cost-effectively monitor structural displacements at multiple points using one camera, widely used interpolation subpixel methods are investigated and incorporated into InnoVision. Third, camera vibration in the field causes displacement measurement errors. A practical solution is proposed by applying the multi-point monitoring to track both the structure and a stationary reference point. The effect of the camera vibration can be canceled by subtracting the reference displacement from the structural displacements. Several laboratory and field tests are conducted to evaluate the InnoVision’s performance. One of the field tests is conducted in a challenging low lighting condition at night on a steel girder bridge to validate the robustness of InnoVision in comparison with two other vision sensing methods. Another field test is carried out on the Manhattan Bridge to demonstrate the efficacy of the proposed technique for canceling camera vibration and the capability of InnoVision to simultaneously monitor multiple points under the effect of camera vibration.

Experimental study on joint stiffness with vision-based system and geometric imperfections of temporary member structure

In this paper, tests of plug-pin joints are conducted in order to obtain their mechanical parameters, including semi-rigid property. To solve the difficulties of multi-point displacement measurements for small joints, this investigation proposes a vision-based measurement system based on the principle of binocular stereo vision to improve measurement accuracy. Accurate sub-pixel location is achieved according to a template-matching algorithm based on grayscale. Joint performance, including horizontal bar joint tension and compression, semi-rigidity between horizontal bars and upright rods and bracing tension and compression, is investigated in order to acquire joint failure modes as well as load and displacement (or moment and rotation angle) curves. Through data fitting, multi-linear simplified models are proposed to illustrate the joints’ mechanical performance. This paper also investigates geometric imperfection of temporary member structure with plug-pin joints based on several substructure models and temporary grandstand units using a total station theodolite. The probabilistic models of initial member out-of-straightness and story frame out-of-plumb have been acquired, which can be used into Monte Carlo simulation to create stochastic model of the temporary member structure.

Sistem Pelacakan Objek Menggunakan Kombinasi Algoritma Optical Flow dan Template Matching

This study aimed to develop a tracking system algorithm by combining optical flow and template matching algorithms to strengthen tracking and minimize the resulting errors. The system has been tested on a football video game with the ball as a tracking object. The optical flow and template matching algorithms are used interchangeably based on the coordinates of both ranges calculated using the Euclidean distance equation. The result shows that the system is able to do tracking, although it is temporarily blocked by other objects, more stable and the distance error between the coordinates of the tracking result and the actual coordinates is not more than 110 pixels compared to using only optical flow or template matching algorithm.

Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis

Accurate delineation of gliomas from the surrounding normal brain areas helps maximize tumor resection and improves outcome. Blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) has been routinely adopted for presurgical mapping of the surrounding functional areas. For completely utilizing such imaging data, here we show the feasibility of using presurgical fMRI for tumor delineation. In particular, we introduce a novel method dedicated to tumor detection based on independent component analysis (ICA) of resting-state fMRI (rs-fMRI) with automatic tumor component identification. Multi-center rs-fMRI data of 32 glioma patients from three centers, plus the additional proof-of-concept data of 28 patients from the fourth center with non-brain musculoskeletal tumors, are fed into individual ICA with different total number of components (TNCs). The best-fitted tumor-related components derived from the optimized TNCs setting are automatically determined based on a new template-matching algorithm. The success rates are 100%, 100% and 93.75% for glioma tissue detection for the three centers, respectively, and 85.19% for musculoskeletal tumor detection. We propose that the high success rate could come from the previously overlooked ability of BOLD rs-fMRI in characterizing the abnormal vascularization, vasomotion and perfusion caused by tumors. Our findings suggest an additional usage of the rs-fMRI for comprehensive presurgical assessment.

Mechanical assembly assistance using marker-less augmented reality system

PurposeThis paper aims to propose a real-time augmented reality (AR)-based assembly assistance system using a coarse-to-fine marker-less tracking strategy. The system automatically adapts to tracking requirement when the topological structure of the assembly changes after each assembly step.Design/methodology/approachThe prototype system’s process can be divided into two stages: the offline preparation stage and online execution stage. In the offline preparation stage, planning results (assembly sequence, parts position, rotation, etc.) and image features [gradient and oriented FAST and rotated BRIEF (ORB)features] are extracted automatically from the assembly planning process. In the online execution stage, too, image features are extracted and matched with those generated offline to compute the camera pose, and planning results stored in XML files are parsed to generate the assembly instructions for manipulators. In the prototype system, the working range of template matching algorithm, LINE-MOD, is first extended by using depth information; then, a fast and robust marker-less tracker that combines the modified LINE-MOD algorithm and ORB tracker is designed to update the camera pose continuously. Furthermore, to track the camera pose stably, a tracking strategy according to the characteristic of assembly is presented herein.FindingsThe tracking accuracy and time of the proposed marker-less tracking approach were evaluated, and the results showed that the tracking method could run at 30 fps and the position and pose tracking accuracy was slightly superior to ARToolKit.Originality/valueThe main contributions of this work are as follows: First, the authors present a coarse-to-fine marker-less tracking method that uses modified state-of-the-art template matching algorithm, LINE-MOD, to find the coarse camera pose. Then, a feature point tracker ORB is activated to calculate the accurate camera pose. The whole tracking pipeline needs, on average, 24.35 ms for each frame, which can satisfy the real-time requirement for AR assembly. On basis of this algorithm, the authors present a generic tracking strategy according to the characteristics of the assembly and develop a generic AR-based assembly assistance platform. Second, the authors present a feature point mismatch-eliminating rule based on the orientation vector. By obtaining stable matching feature points, the proposed system can achieve accurate tracking results. The evaluation of the camera position and pose tracking accuracy result show that the study’s method is slightly superior to ARToolKit markers.

Simultaneous location and size measurement of particles using extended glare-point imaging technique.

Interferometric particle imaging (IPI) is a robust and popular technique for measuring particle size and velocity. A method based on a template matching algorithm and an auto-correlation method is proposed to simultaneously extract the location and the separation of doublet images of a particle from an IPI focused image. The position coordinate (x,y) of the particle can be determined with high accuracy, as evaluated by using a serial particle mask. Furthermore, the method can be employed to achieve sub-pixel spacing extraction when combined with Gaussian interpolation. The algorithm is tested using synthetic and experimental data. The results suggest that the method presented here is promising for its application to a high-density particle field, in accurately measuring both the particle size and its location.

Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull

To propose an algorithm for automatic localization of 3D cephalometric landmarks on CBCT data, those are useful for both cephalometric and upper airway volumetric analysis. 20 landmarks were targeted for automatic detection, of which 12 landmarks exist on the mid-sagittal plane. Automatic detection of mid-sagittal plane from the volume is a challenging task. Mid-sagittal plane is detected by extraction of statistical parameters of the symmetrical features of the skull. The mid-sagittal plane is partitioned into four quadrants based on the boundary definitions extracted from the human anatomy. Template matching algorithm is applied on the mid-sagittal plane to identify the region of interest ROI, further the edge features are extracted, to form contours in the individual regions. The landmarks are automatically localized by using the extracted knowledge of anatomical definitions of the landmarks. The overall mean error for detection of 20 landmarks was 1.88 mm with a standard deviation of 1.10 mm.The cephalometric landmarks on CBCT data were detected automaticallywith in the mean error less than 2mm.

A Posteriori Processing Optimization Method for Gesture Interaction in Aseptic Operation Room

Gesture recognition plays an important role in the applications of human-centered smart technologies. This paper designs a gesture recognition approach in a special human-computer interaction environment. Generally, in an aseptic operation room, doctors cannot access the patient’s image data directly, and they have to let the nurse operate the system under their instructions. After the nurses finish their task, they also need to re-dress the surgical gown, disinfect their hands, then they can go back to the operating room. If the doctor could access the image directly by gestures during the operation, they would be able to save a lot of time and work more efficiently. Although some template matching algorithms, such as dynamic time warping (DTW), have been widely used in gesture recognition, those traditional methods are not accurate enough, and they are unable to identify continuous repetitive gestures. This paper proposes an improved DTW method based on posteriori processing, which achieved some key operations, including parameter regulations, invalid area determination, and static gesture processing. This method will address the problem of continuous repetitive gesture recognition, with higher accuracy. The experimental results show that the method can improve the accuracy of recognition by over 40%, and it can also perform real-time dynamic gesture recognition tasks effectively.

Feature Extraction and Template Matching Algorithms Classification for PCB Fiducial Verification

Automatic Optical Inspection (AOI) systems that are extensively used in the industry of Electronics Manufacturing Services (EMS), performs the inspection of Surface Mount Devices (SMD). One of the main tasks of such an AOI system is to align a given PCB to the parameters of the corresponding PCB positioning system by a process called fiducial alignment. However, no detailed analysis has been carried out so far on the methodologies that can be used to have a very precise identification of PCB fiducial points. In our research, we have implemented an AOI system for the inspection of soldering defects of Through Hole Technology (THT) solder joints, which can be integrated to a desktop soldering robotic platform. Such platforms are used in environments where no specific lighting conditions can be provided within a surrounded atmosphere. Therefore, an AOI system that is capable of performing fiducial alignment of any given PCB under varying lighting condition is highly preferred. In this paper, we have presented a detailed analysis on feature extraction and template matching algorithms that can be used to implement a very precise fiducial verification process under normal lighting condition.

基于快速仿射模板匹配和改进仿射迭代最近点算法的人脸稳健精确仿射配准

基于快速仿射模板匹配和改进仿射迭代最近点算法的人脸稳健精确仿射配准

On-line Maneuver Identification in Highway Traffic Using Elastic Template Matching

Early identification and interpretation of traffic maneuvers will become key elements of modern driver assistance systems. Currently, only simple traffic situations can be detected by using a decision tree (DT) procedure in most cases. Even though this approach is straightforward, it still requires an expert to build a different DT for every maneuver. Moreover, for complex driving behaviours such simple logic identification does not work. In the present work we develop a template (or pattern) matching (TM) algorithm for maneuver recognition in highway traffic with a particular focus on a cut-in maneuver. By using maneuver patterns we are able to consider scenarios of arbitrary complexity and make the identification procedure more systematic and with minimal man expert involvement. The key element of the proposed TM algorithm is the settlement of a distance measure. We use dynamic time warping (DTW) method for an optimal non-linear alignment between two sequences under certain restrictions. It enables a correct assignment of the same maneuver but having different dynamics (speed, aggressiveness, etc.), and is robust to data with missing values. We illustrate the proposed method by cut-in detection from real traffic data and compare it to the standard DT identification.

Study on licence plate location algorithm in complex weather

An adaptive licence plate location algorithm is proposed to improve locating accuracy of licence plates in the case of images collected in conditions of complex weather and insufficient light. The algorithm judges the different weather images by colour features and sharpness. The wavelet coefficients are used to adjust the contrast of image. At last, we fuse the vertical protection and template matching algorithms to locate licence plate. Experiments show the algorithm can locate licence plate in varied weathers, such as sunny, rainy, foggy and evening. The average rate of licence plate location can reach 93.4%.

Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Undersampled Data in Magnetic Resonance Fingerprinting (MRF).

Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging technique that allows simultaneous measurements of multiple important tissue properties in human body, e.g., T1 and T2 relaxation times. While MRF has demonstrated better scan efficiency as compared to conventional quantitative imaging techniques, further acceleration is desired, especially for certain subjects such as infants and young children. However, the conventional MRF framework only uses a simple template matching algorithm to quantify tissue properties, without considering the underlying spatial association among pixels in MRF signals. In this work, we aim to accelerate MRF acquisition by developing a new post-processing method that allows accurate quantification of tissue properties with fewer sampling data. Moreover, to improve the accuracy in quantification, the MRF signals from multiple surrounding pixels are used together to better estimate tissue properties at the central target pixel, which was simply done with the signal only from the target pixel in the original template matching method. In particular, a deep learning model, i.e., U-Net, is used to learn the mapping from the MRF signal evolutions to the tissue property map. To further reduce the network size of U-Net, principal component analysis (PCA) is used to reduce the dimensionality of the input signals. Based on in vivo brain data, our method can achieve accurate quantification for both T1 and T2 by using only 25% time points, which are four times of acceleration in data acquisition compared to the original template matching method.

Object Tracking Test Automation Using a Robotic Arm

Touch-to-track is a software feature that is used to track objects in embedded systems, such as mobile phones. The research question that is discussed in the paper is as follows: how can one design an automated test method for accurately testing object tracking algorithms? The challenge for the verification team was to design a method of test automation for testing this object tracking feature. The solution to the problem was to develop test automation algorithms using a robotic arm to accurately test this software feature. The robotic arm is used for executing test cases for tracking single or multiple objects in motion. To use the touch-to-track feature, the user selects an area in the camera preview by drawing a bounding box. Hough transformation and color segmentation are applied to accurately detect the bounding box drawn on every frame in which the object is detected. The area inside the bounding box is considered as the template. Template matching algorithms based on normalized cross-correlation, phase correlation, and speeded up robust features (SURF)-based feature extraction and matching are then applied to compare the template and original images. The tracking efficiency is calculated by dividing the total number of frames that contain the object being tracked by the total number of frames being tested. The tracking efficiencies achieved with the Hough-based bounding box detection algorithm followed by template matching algorithms based on normalized cross-correlation, phase correlation, and SURF-based feature extraction and matching are compared. These tracking efficiencies are calculated by comparing the ground truth to the tracking results for each frame. The tracking efficiency calculated for three objects is 87.7% with no colored background. The proposed object tracking solution with hardware acceleration is found to perform better than the third-party solution with respect to the latency in re-establishing tracking and the hand jitter scenario.

Template matching approach for automatic human body tracking in video

In this paper a novel template matching approach is presented to achieve automatic human body tracking in video sequences. The developed method which is based on a special template matching algorithm applied on a set of interest points detected on the human body contour. The matching approach is based on different types of similarity measures applied on consecutive frames from videos. Each frame was attacked with different types of noise: luminosity variation and motion blur. This new approach considers different matching constraints such as: cross-matching, uniqueness constraint and interest point's appearances and disappearances between consecutive frames. The algorithm was validated on two different datasets and the obtained results are so encouraging with high values of matching rate and good tracking rate.

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%.