Adaptive Template Matching Research Articles

A novel method for multiple targets localization based on normalized cross-correlation adaptive variable step-size dynamic template matching

The template matching method has been widely utilized in the defect detection of wafer surfaces. However, the traditional matching approaches are limited by illumination, noise, and deformation, which cannot meet the requirements of accuracy and robustness. In this paper, a novel multiple targets localization method, named Normalized Cross-correlation Adaptive Variable Step-Size Dynamic Template (NCC-AVSSDT) matching, is proposed to improve the accuracy and efficiency of image localization, which combines the advantages of NCC and AVSSDT. The AVSSDT method is utilized to dynamically adjust the scanning step size based on the NCC matching coefficients. This approach optimizes the scanning process, accelerating convergence toward the optimal matching position. Experimental results verify the accuracy and robustness of the proposed method under different conditions, especially when dealing with rotational variations and variations in noise textures. Therefore, NCC-AVSSDT can be used to perform multiple targets localization of chip image in nearly real-time. Three experiment types were used for comprehensive evaluations, including multiple targets, noise, and rotation angles. Experimental results show that NCC-AVSSDT is much better than the sequential similarity detection algorithm and mean absolute deviation methods in terms of multiple targets (0.667 vs 0.811 s, 0.832 s) and success rate (100% vs 35%, 20%).

An effective premature ventricular contraction detection algorithm based on adaptive template matching and characteristic recognition

An effective premature ventricular contraction detection algorithm based on adaptive template matching and characteristic recognition

Heart rate detection of ballistocardiogram based on improved DAE and template matching method

Ballistocardiogram (BCG) is a non-invasive physiological signal detection method that can be used for non-contact detection of resting heart rate (RHR) and has been widely used in human health monitoring. However, the BCG signal is vulnerable to noise, making it challenging to accurately measure heart rate. In this paper, we propose a noise reduction model for the BCG signal based on Gramian Angular Field (GAF) and an improved Denoising Autoencoder (DAE), referred to as the GDAE model, to accurately detect heart rate in noise-contaminated signals. First, the Gramian Angular Field transform is used to convert the one-dimensional BCG signal into two-dimensional image information, highlighting the difference between the signal centroid information and noise information; after that, the transformed image is denoised by the Denoising Autoencoder to obtain a denoised BCG signal. After noise reduction, the heart rate of the subject is calculated using the adaptive template matching method. The test proves that under the strong noise interference, the proposed method improves the recall by 6.87% and the accuracy by 6.02% compared with the traditional method, indicating a better detection effect. Furthermore, the comparison test shows that the GDAE model has a significant noise reduction effect on the BCG signal, which improves the practicality of the BCG method for heart rate detection.

IC solder joint inspection via adaptive statistical modeling

Purpose Statistical modeling has been successfully applied to integrated circuit (IC) solder joint inspection. However, there are some inherent problems in previous statistical modeling methods. This paper aims to propose an adaptive statistical modeling method to further improve the inspection performance for IC solder joints. Design/methodology/approach First, different pixels in the IC solder joint image were modeled by different templates, each of which was composed of the hue value of the pixel and a proposed template significance factor. Then, the potential defect image was obtained by adaptive template matching and the potential defect threshold for each pixel. It was noted that the number of templates, matching distance threshold, potential defect threshold and updating rate were adaptively updated during model training. Finally, the trained statistical model was used to inspect the IC solder joints by means of defect degree. Findings Experimental results indicated that the proposed adaptive schemes greatly contributed to the inspection performance of statistical modeling. Also, the proposed inspection method achieved better performance compared with some state-of-the-art inspection methods. Originality/value The proposed method offers a promising approach for IC solder joint inspection, which establishes different numbers of templates constructed by pixel values and template significance factors for different pixels. Also, some important parameters were adaptively updated with the updating of the model, which contributed to the inspection performance of the model.

Fusion-Based Versatile Video Coding Intra Prediction Algorithm with Template Matching and Linear Prediction.

The new generation video coding standard Versatile Video Coding (VVC) has adopted many novel technologies to improve compression performance, and consequently, remarkable results have been achieved. In practical applications, less data, in terms of bitrate, would reduce the burden of the sensors and improve their performance. Hence, to further enhance the intra compression performance of VVC, we propose a fusion-based intra prediction algorithm in this paper. Specifically, to better predict areas with similar texture information, we propose a fusion-based adaptive template matching method, which directly takes the error between reference and objective templates into account. Furthermore, to better utilize the correlation between reference pixels and the pixels to be predicted, we propose a fusion-based linear prediction method, which can compensate for the deficiency of single linear prediction. We implemented our algorithm on top of the VVC Test Model (VTM) 9.1. When compared with the VVC, our proposed fusion-based algorithm saves a bitrate of 0.89%, 0.84%, and 0.90% on average for the Y, Cb, and Cr components, respectively. In addition, when compared with some other existing works, our algorithm showed superior performance in bitrate savings.

Real-time Driver Drowsiness Detection with Android

Abstract: Major causes for the road accidents are Driver Fatigue and Distraction. Recently many driver monitoring systems have been proposed for monitoring driver activities to avoid accidents. Almost all the systems which exists till date are in the specialized embedded or hardware form, which are present in expensive and luxurious cars. This research paper presents a cost efficient approach to detect driver fatigue and monitor dizziness/drowsiness in real-time. An intelligent android app to monitor driver fatigue and distraction during travel using Adaptive Template Matching and Adaptive Boosting is achieved and implemented. Developing of technologies for preventing drowsiness is quite challenging in the area of accident avoiding systems. Preventing drowsiness during driving requires a method to accurately detect lowering of driver attentiveness and a method for alerting and to forewarn the driver. In this detection approach, we developed an android app that utilizes image processing technology and analyse all the images captured through the user’s phone camera. Drop of alertness is detected based on the degree to which the driver's eyes are open or closed. This application provides a non-contact method for judging various levels of driver awareness and enables an early detection of a reduction in attentiveness while driving. Keywords: Drowsiness Detection, Google vision API, Face detection, Eye Detection, Android application

An adaptive segmentation and template matching based on SSA for interference suppression in millimeter wave radar

The mutual interference between multiple-input multiple-output (MIMO) millimeter wave (mmWave) radars reduces the signal-to-noise ratio (SNR) of the target, which in turn affects the angle estimation of the target. The singular spectrum analysis (SSA) can be used to suppress the interference by signal reconstruction. However, when there is interference in the intermediate frequency (IF) signal, it is hard to select the principal components (PCs) to reconstruct the IF signal. Therefore, an adaptive segmentation and template matching based on SSA (ASTM-SSA) is proposed. In the adaptive segmentation, the IF signal is divided into interference and interference-free segments based on the location of the interference. In the template matching, the desired PCs of the interference-free segment are considered as the templates to match with PCs of the interference segment, and the matched PCs are used to reconstruct the IF signal. After all the interference are suppressed, the classical multiple signal classification (MUSIC) is performed to estimate the angle of the target. The simulation and field experiments are conducted to verify the effectiveness of the ASTM-SSA, and the results indicate that the ASTM-SSA can effectively suppress interference and accurately estimate the angle of the target.

Recognition Method for Face Local Features Based on Fuzzy Algorithm and Intelligent Data Analysis

Traditional recognition methods for face local features have a low recognition rate, so the recognition method for face local features based on the fuzzy algorithm and intelligent data analysis was designed. Firstly, the wavelet denoising method was used to reduce the noise of face images, and adaptive template matching was performed on the obtained images. Then, the face information was encoded, and the face features were identified to locate the face features. On this basis, the principal components of the face were analyzed to obtain the global features of the face. Finally, through the candidate set of facial local feature recognition, the extraction of face local features, and the fusion of face local features, the recognition of local face features were realized. The experimental results show that the average recognition rate of this method is 88.84% in a noise environment and 97.3% in noise‐free environment. It can accurately recognize the face local features, can meet the needs of recognition of the face local features, and has certain practical application significance.

Improved Spike Detection Algorithm Based on Multi-Template Matching and Feature Extraction

Spike detection is of great significance in the detection of epileptic seizures. Many spike detection algorithms have been proposed, but available algorithms often miss spikes during high firing rate epochs or with artifacts. Also, there exists methodological limitations when there are great variations in spike morphology between different patients or the same patient in different EEG epoch. Thus a data-driven spike detection method using multi-template matching, feature extraction and threshold method is proposed in this work to improve the detection performance under different circumstances. First, universal template matching and feature extraction are used to get putative spikes. Then clustering algorithm and adaptive template matching algorithm are applied to maximize the number of detected spikes for these specific candidate single units obtained from the clustering method. In addition, a data-driven approach based on receiver operating characteristic (ROC) is used for low false identification rate detection. The performance is evaluated on real EEG samples and the evaluating results shows that this algorithm has achieved 97.12% average sensitivity and 0.55 average false negative rate per minute. The least sensitivity is 94.32% and the most false negative rate per minute is 1.23.

Unconstrained Accurate Beat-to-beat Heart Rate Extraction Based on Piezoelectric Ceramics Sensor

BallistoCardioGram (BCG) can be used for contactless detection of vital signs. In BCG’s beat-to-beat heart rate extraction, the lower mean absolute error is of great significance for accurately obtaining the user’s Heart Rate Variability (HRV) indicators. In order to solve the shortcomings in the accuracy of beat-to-beat heart rate calculation of most current methods, a BCG acquisition system based on piezoelectric ceramics sensor is designed in this paper. By adopting a suitable structure for the sensor‘s shell and a suitable sampling frequency, the sensitivity of the sensor and the time resolution of the BCG signal are increased. Through the analysis of BCG, the most suitable components in BCG is found to extract beat by beat cardiac cycle. At the same time, this paper proposes an adaptive template matching algorithm using AP clustering to extract accurately cardiac cycle information. Analysis of the data of 5741 heartbeats of 15 subjects shows that the average error of the beat-to-beat heartbeat cycle is 0.48%, the Mean Absolute Error (MAE) is 3.78 ms, and the heartbeat coverage is above 95%, which is better than other similar work.

Detection Method of Three-Dimensional Echocardiography Based on Deep Learning

In order to improve the detection and recognition ability of 3D echocardiography, a method of 3D echocardiography detection based on depth learning is proposed. The information conduction model of three-dimensional echocardiography is constructed. The edge pixel feature matching method is used to extract the key information of echocardiography, and the information compensation method is used to repair the missing area of three-dimensional echocardiography information. The feature decomposition and information fusion of 3D ultrasonic imaging are carried out by using five stage wavelet decomposition method, and the feature reconstruction and adaptive template matching of 3D echocardiography are processed by depth learning algorithm, modeling and detecting the rationality of three-dimensional echocardiography. The simulation results show that this method has better detection performance; the accuracy of detection and recognition is high, which is more reasonable in the application of 3D echocardiography repair and detection recognition.

Automated MV markerless tumor tracking for VMAT

Tumor tracking during radiotherapy treatment can improve dose accuracy, conformity and sparing of healthy tissue. Many methods have been introduced to tackle this challenge utilizing multiple imaging modalities, including a template matching based approach using the megavoltage (MV) on-board portal imager demonstrated on 3D conformal treatments. However, the complexity of treatments is evolving with the introduction of VMAT and IMRT, and successful motion management is becoming more important due to a trend towards hypofractionation.We have developed a markerless lung tumor tracking algorithm, utilizing the electronic portal imager (EPID) of the treatment machine. The algorithm has been specifically adapted to track during complex treatment deliveries with gantry and MLC motion. The core of the algorithm is an adaptive template matching method that relies on template stability metrics and local relative orientations to perform multiple feature tracking simultaneously. Only a single image is required to initialize the algorithm and features are automatically added, modified or removed in response to the input images. This algorithm was evaluated against images collected during VMAT arcs of a dynamic thorax phantom. Dynamic phantom images were collected during radiation delivery for multiple lung SBRT breathing traces and an example patient data set. The tracking error was 1.34 mm for the phantom data and 0.68 mm for the patient data.A multi-region, markerless tracking algorithm has been developed, capable of tracking multiple features simultaneously without requiring any other a priori information. This novel approach delivers robust target localization during complex treatment delivery. The reported tracking error is similar to previous reports for 3D conformal treatments.

Heuristic Analysis for In-Plane Non-Contact Calibration of Rulers Using Mask R-CNN

Determining an object measurement is a challenging task without having a well-defined reference. When a ruler is placed in the same plane of an object being measured it can serve as metric reference, thus a measurement system can be defined and calibrated to correlate actual dimensions with pixels contained in an image. This paper describes a system for non-contact object measurement by sensing and assessing the distinct spatial frequency of the graduations on a ruler. The approach presented leverages Deep Learning methods, specifically Mask Region proposal based Convolutional Neural Networks (R-CNN), for rulers’ recognition and segmentation, as well as several other computer vision (CV) methods such as adaptive thresholding and template matching. We developed a heuristic analytical method for calibrating an image by applying several filters to extract the spatial frequencies corresponding to the ticks on a given ruler. We propose an automated in-plane optical scaling calibration system for non-contact measurement.

Repeat pattern segmentation of print fabric based on adaptive template matching

Patterned fabrics are generally constructed from the periodic repetition of a primitive pattern unit. Repeat pattern segmentation of printed fabrics has a very significant impact on the pattern retrieval and pattern defect detection. In this paper, we propose a new approach for repeat pattern segmentation by employing the adaptive template matching method. In contrast to the traditional method for template matching, the proposed algorithm first selects an adaptive size template image in the repeat pattern image based on the size of the original image and its local maximum edge density. Then it uses the sum of absolute differences as the matching features to identify the matched regions in the original image, and the minimum envelope border of the primitive pattern, typically as a parallelogram, can be determined from the results of the four adjacent matched templates. Finally, image traversal base on the obtained parallelogram is implemented over the original image using minimum information loss theory to produce a well-segmented primitive pattern with a complete edge structure. The results from the experiments conducted using an extensive database of real fabric images show that the proposed algorithm has the advantage of rotation invariance and scaling invariance and will not be affected when the background or foreground color is changed.

Text area segmentation from document images by novel adaptive thresholding and template matching using texture cues

This paper presents a new perspective of text area segmentation from document images using a novel adaptive thresholding for image enhancement. Using sliding windows, the texture of the enhanced image is matched with that of a fixed training template image containing the typed letters ‘dB.’ The affine-invariant, low-dimensional difference theoretic texture feature set is used for the texture measurement. The distance matrix is binarized using Otsu threshold, and the ‘0’ pixels indicate the text area. One primary contribution of this paper is the novel adaptive thresholding for document image enhancement prior to the extraction of texture cues. The proposed adaptive thresholding mimics the ability of the human eye to iteratively adjust to varying light intensities through iterative gamma correction followed by contrast stretching so that the text becomes well defined against the background clutter. The text blobs so segmented are binarized using Yanowitz and Bruckstein method of text binarization, and the results are applied for evaluation with respect to the ground-truth annotations. We tested our algorithm on the benchmark DIBCO 2009, 2010, 2011, 2012, 2013 document image datasets in comparison with the state of the art. The high precision–recall and F-score values establish the efficiency of our approach.

Spinal cord detection in planning CT for radiotherapy through adaptive template matching, IMSLIC and convolutional neural networks

Background and ObjectiveThe spinal cord is a very important organ that must be protected in treatments of radiotherapy (RT), considered an organ at risk (OAR). Excess rays associated with the spinal cord can cause irreversible diseases in patients who are undergoing radiotherapy. For the planning of treatments with RT, computed tomography (CT) scans are commonly used to delimit the OARs and to analyze the impact of rays in these organs. Delimiting these OARs take a lot of time from medical specialists, plus the fact that involves a large team of professionals. Moreover, this task made slice-by-slice becomes an exhaustive and consequently subject to errors, especially in organs such as the spinal cord, which extend through several slices of the CT and requires precise segmentation. Thus, we propose, in this work, a computational methodology capable of detecting spinal cord in planning CT images. MethodsThe techniques highlighted in this methodology are adaptive template matching for initial segmentation, intrinsic manifold simple linear iterative clustering (IMSLIC) for candidate segmentation and convolutional neural networks (CNN) for candidate classification, that consists of four steps: (1) images acquisition, (2) initial segmentation, (3) candidates segmentation and (4) candidates classification. ResultsThe methodology was applied on 36 planning CT images provided by The Cancer Imaging Archive, and achieved an accuracy of 92.55%, specificity of 92.87% and sensitivity of 89.23% with 0.065 of false positives per images, without any false positives reduction technique, in detection of spinal cord. ConclusionsIt is demonstrated the feasibility of the analysis of planning CT images using IMSLIC and convolutional neural network techniques to achieve success in detection of spinal cord regions.

A System for Real-Time Syringe Classification and Volume Measurement Using a Combination of Image Processing and Artificial Neural Networks

The purpose of this research was to develop a system that can read and report the volume of liquid medication present in syringes. The system is comprised of a digital webcam which is designed to communicate with a computer program developed using MATLAB. The system includes two functional modules, one supporting syringe classification, and another supporting volume measurement. Adaptive template matching was used to determine the best match point between target and template images. The connected component labeling method was used during volume measurement. An artificial neural network (ANN) model was developed using MATLAB to support the intended volume measurement functionality. The developed ANN was designed as a classifier which determines the plunger depth of the syringe and then leverages this result to calculate and derive the volume of medication inside the syringe as the final system output. Commercial Luer-lock syringes of sizes from 1 to 30 ml were used in conjunction with syringe tip caps of blue and yellow color. Tap water or aqueous dye solutions of yellow, red, and blue color simulated liquid medication in the syringe. The developed syringe classification system successfully detected and categorized all tested syringes according to their size. The best accuracy of the system was found to be 99.95% with a 3-ml syringe, while the worst accuracy was 95.82% with a 5-ml syringe. It took approximately 6 s to perform the entire task demonstrating the utility of this system to report volumes in real time. The developed system can be used across a variety of settings that routinely support measuring and handling liquids in syringes including hospitals, pharmacies, and the pharmaceutical industry.

Adaptive template matching of photoplethysmogram pulses to detect motion artefact

Objective: The photoplethysmography (PPG) signal, commonly used in the healthcare settings, is easily affected by movement artefact leading to errors in the extracted heart rate and SpO2 estimates. This study aims to develop an online artefact detection system based on adaptive (dynamic) template matching, suitable for continuous PPG monitoring during daily living activities or in the intensive care units (ICUs). Approach: Several master templates are initially generated by applying principal component analysis to data obtained from the PhysioNet MIMIC II database. The master template is then updated with each incoming clean PPG pulse. The correlation coefficient is used to classify the PPG pulse into either good or bad quality categories. The performance of our algorithm was evaluated using data obtained from two different sources: (i) our own data collected from 19 healthy subjects using the wearable Sotera Visi Mobile system (Sotera Wireless Inc.) as they performed various movement types; and (ii) ICU data provided by the PhysioNet MIMIC II database. The developed algorithm was evaluated against a manually annotated ‘gold standard’ (GS). Main results: Our algorithm achieved an overall accuracy of 91.5% ± 2.9%, with a sensitivity of 94.1% ± 2.7% and a specificity of 89.7% ± 5.1%, when tested on our own data. When applying the algorithm to data from the PhysioNet MIMIC II database, it achieved an accuracy of 98.0%, with a sensitivity and specificity of 99.0% and 96.1%, respectively. Significance: The proposed method is simple and robust against individual variations in the PPG characteristics, thus making it suitable for a diverse range of datasets. Integration of the proposed artefact detection technique into remote monitoring devices could enhance reliability of the PPG-derived physiological parameters.

Detection of ball grid array solder joints based on adaptive template matching

Detection of ball grid array solder joints based on adaptive template matching

Integrating Local Binary Patterns into Normalized Moment of Inertia for Updating Tracking Templates

This paper presents an efficient visual tracking framework which is robust to rotation, scale variation and occlusion. The target template is characterized by Local binary patterns (LBP), which exhibit invariance to rotation. The LBP features are then integrated into the Normalized moment of inertia (NMI) to decide whether the template requires update. This procedure enables an adaptive template matching strategy which addresses the tracking failures arising from scale variations. Kalman filtering is exploited for predicting the trajectory of the target when it is occluded. The matching efficiency is achieved by applying a locally pyramid searching scheme. Experimental results validate the efficiency and effectiveness of our tracking framework.