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As a direct and effective biometric technology that follows human life habits, facial recognition has gradually become a mainstream, stable and reliable recognition method in the process of further development of science and technology. Facial recognition is a kind of biometric authentication based on recognition technology that is an original biological characteristic. After collecting the biometric functions, use a computer for digital image processing and template matching to complete the process of facial recognition. In the face of MOOC, establish a high-quality resource sharing mechanism, use this opportunity to explore innovative education models, and truly feel the strong momentum of traditional Chinese higher education, which has greatly improved the quality of education in Chinese universities. In addition, in order to vigorously promote the internationalization of education in China, education scholars need to conduct a lot of in-depth research on MOOC. In this article, a demand-based testing method is used to establish a skin color distribution model for color image preprocessing, and then develop and construct according to the comprehensive analysis of the university English class in the MOOC platform implementation mechanism (including the establishment of basic principles of operation mechanism), thereby become a guarantee and support for curriculum and education quality evaluation system. This article combines the characteristics of educational practice and MOOC education, takes college English courses as an example, studies its application mechanism, builds a MOOC platform, and continuously enhances students' interest in learning English, aiming to provide a practical reference for the reform of Chinese college English education. [ABSTRACT FROM AUTHOR]

Template matching is a process to identify and localize a template image on an original image. Several methods are commonly used for template matching, one of which uses the Fourier transform. This study proposes a modification of the method by adding an improved rotation to the Fourier transform. Improved rotation in this study uses increment rotation and three shear methods for the template image rotation process. The three shear rotation method has the advantage of precise and noisefree rotation results, making the template matching process even more accurate. Based on the experimental results, the use of 10°angle increments has increased template matching accuracy. In addition, the use of three shear rotations can improve the accuracy of template matching by 13% without prolonging the processing time. [ABSTRACT FROM AUTHOR]

The detection of earthquake signals is a fundamental yet challenging task in observational seismology. A robust automatic earthquake detection algorithm is strongly demanded in view of the ever‐growing global seismic dataset. Here, we develop an automatic earthquake detection framework based on a deep learning approach (SCALODEEP). It extracts high‐order features embedded in three‐component seismograms by encoding a time‐frequency representation of the data (scalogram) into a deep network with skip connections. The SCALODEEP is trained and validated on an open‐source dataset from North California, and then employed to seismicity detection in four areas, including Arkansas, Japan, Texas, and Egypt. Despite vastly varying characteristics of regional earthquakes (e.g., focal mechanism, duration, and noise level), SCALODEEP successfully detects seismic signals over a broad range of local magnitudes (as low as −1.3ML) and outperforms conventional algorithms such as STA/LTA, FAST, and template matching. Compared to recently proposed deep learning based frameworks (e.g., CRED and Earthquake transformer), SCALODEEP achieves a superior generalization ability via a sophisticated network architecture. In summary, our study offers a promising new tool to improve existing earthquake detection systems and, as importantly, sheds light on designing an effective deep learning network for generalized earthquake detection. Plain Language Summary: An efficient and reliable earthquake detection based on deep learning is of great interest to a broad scope of geoscience community. One question that has been constantly raised by seismic practitioners is whether/how we can generalize the trained network to be widely applicable. We introduce a new deep learning method for generalized earthquake detection. Our network includes a very deep architecture with 24,629,053 parameters, and its generalization ability is further augmented by implementations of time‐frequency representation of seismic data and skip connections. We compare the performance of the new framework with the state‐of‐the‐art FAST, template matching, and CRED methods, and demonstrate its advantages. To demonstrate the potential in practical usage, we train the network using a community dataset from North California, and test its generalization ability on four independent regional datasets from Arkansas, Japan, Texas, and Egypt. Key Points: We introduce a new deep learning method, SCALODEEP, for generalized earthquake detectionWe compare the performance of the new framework with the state‐of‐the‐art FAST, template matching, and CRED methods, and demonstrate its advantagesApplications of the SCALODEEP to four distinctive seismogenic zones indicate its superior generalization capability [ABSTRACT FROM AUTHOR]

This paper introduces an improved algorithm for texture-less object detection and pose estimation in industrial scenes. In the template training stage, a multi-scale template training method is proposed to improve the sensitivity of LineMOD to template depth. When this method performs template matching, the test image is first divided into several regions, and then training templates with similar depth are selected according to the depth of each test image region. In this way, without traversing all the templates, the depth of the template used by the algorithm during template matching is kept close to the depth of the target object, which improves the speed of the algorithm while ensuring that the accuracy of recognition will not decrease. In addition, this paper also proposes a method called coarse positioning of objects. The method avoids a lot of useless matching operations, and further improves the speed of the algorithm. The experimental results show that the improved LineMOD algorithm in this paper can effectively solve the algorithm's template depth sensitivity problem. [ABSTRACT FROM AUTHOR]

It is difficult to achieve the requirements simultaneously of real‐time operation and accuracy when the template matching method encounters the problems of rotation and scale invariance. This letter proposes a dual‐evaluation multiscale template‐matching algorithm based on wavelet transform. First, the image grids generated from a strengthened edge image based on the wavelet transform are defined to reduce the region for detecting feature points. Then, an evaluation strategy based on gradient direction entropy is proposed to evaluate the local pixel for detecting local candidate points that contain rich information. Another evaluation strategy, based on the dominant gradient direction and the uniform LBP model, has been proposed to extract rotation‐invariant feature pixels. To conduct fast and robust matching, the proposed strategies extract fewer feature points with rich local information. The experimental results demonstrate that the proposed method is robust to rotation and scale invariance and achieves real‐time accuracy. [ABSTRACT FROM AUTHOR]

Counting trees can be challenging due to the crowded environment, time-consuming, and expensive operation. The information on the locations and the number of oil palm trees in a plantation area is important in many aspects. First, it is important to predict the yield of palm oil, which is the most widely used vegetable oil in the world. Second, it provides essential information to understand the growing situation of palm trees after plantation, such as the age or the survival rate of the palm trees. As such, this research investigated tree counting extraction of oil palm plantation. The research area is located at an oil palm plantation area (Felda Pasir Raja) in Johor, Malaysia. Three methods of extraction had been used in this research, i.e. Template Matching Algorithm (TMA), ISO Cluster Unsupervised Classification (ICUC), and Tree Canopy Segmentation (TCS). The results obtained using TCS emerged as the best method for tree counting in this research. The number of the trees detected by the TCS method was 77,963 trees, while its percentage was 96%. As for TMA and ICUC, the percentages were 89% and 82%, respectively. Therefore, this research could be used amongst the plantation organisations, especially the oil palm industries, which are responsible to monitor the status of oil palm trees for effective palm oil production. [ABSTRACT FROM AUTHOR]

The observation of a transient slip 1 month before the rupture of the 2011 Tohoku earthquake is a conandrum since the area was supposedly fully coupled. A better understanding of the mechanisms at work during the preseismic phase is thus fundamental. However, the configuration of the Pacific plate and the location of the Tohoku rupture zone 200 km from the coast make it difficult to detect microseismic events. In this study, we use a multidimensional template matching (MDTM) technique to detect earthquakes that are hidden in the noise. The temporal distribution of these 395 newly detected earthquakes provides new insights on the slip history of the megathrust earthquake epicentral zone. The detected events can be separated into two groups: 187 low‐frequency detections (below 5 Hz) that well recorded the episodes of earthquake migration prior to the Tohoku earthquake and 208 high‐frequency detections (above 10 Hz) that occurred close to the rupture zones of the M ≥ 4.8–6 earthquakes that struck between the 9 March 2011 M7.3 foreshock and the 30 November 2010 Tohoku‐Oki earthquake. The seismic rate of these high frequency detection events starts to increase on 30 November 2010 until the Tohoku earthquake. Key Points: We developed a multidimensional template matching technique to detect small seismic eventsEvents detected at low frequency (1–10 Hz) see the migration episode before the Tohoku event and are located in potential creeping zonesThe rate of events detected at high frequency (>14 Hz) increases from November 2010 to the Tohoku earthquake [ABSTRACT FROM AUTHOR]

Faces blurring is one of the important complex processes that is considered one of the advanced computer vision fields. The face blurring processes generally have two main steps to be done. The first step has detected the faces that appear in the frames while the second step is tracking the detected faces which based on the information extracted during the detection step. In the proposed method, an image is captured by the camera in real time, then the Viola Jones algorithm used for the purpose of detecting multiple faces in the captured image and for the purpose of reducing the time consumed to handle the entire captured image, the image background is removed and only the motion areas are processed. After detecting the faces, the Color-Space algorithm is used to tracks the detected faces depending on the color of the face and to check the differences between the faces the Template Matching algorithm was used to reduce the processes time. Finally, the detected faces as well as the faces that were tracked based on their color were obscured by the use of the Gaussian filter. The achieved accuracy for a single face and dynamic background are about 82.8% and 76.3% respectively. [ABSTRACT FROM AUTHOR]

Highlights • Displacements tracking with nanometric accuracy at the frame rate of hundreds hertz. • An accelerated inverse optimized searching (AIOS) algorithm is presented. • Abundant simulation tests and the experimental tests are conducted. Abstract This study presents a micro-vision-based measurement method for realtime and high accuracy in-plane displacements tracking of the precision positioning stage. In this method, to realize high magnification ratio and high video rate imaging of the measured stage's surface, a micro-vision imaging system is established. In addition, an accelerated inverse optimized searching (AIOS) algorithm is developed in which an optimal template chosen (OTC) strategy and a piecewise update (PU) scheme are proposed and integrated with the IOS method to achieve template matching with high accuracy and high frame rate. Simulation studies are presented to demonstrate the validity of the OTC strategy, PU scheme, and AIOS algorithm, respectively. Experimental results demonstrate that the proposed method can effectively realize displacement measurement of the precision positioning stage at a frequency of hundreds Hertz with accuracy of nanometer degree. [ABSTRACT FROM AUTHOR]

A new method is introduced which tolerates distortions between spectrogram patterns in template matching. Cross-correlation provides a fast method to calculate similarities but it is sensitive to durational and spectral fluctuations. Dynamic time warping tolerates durational differences but requires that frequency components match. An extension to dynamic time warping is introduced where frequency is warped in addition to time. This allows for a detailed analysis to find the similarities and differences between the sound units to be compared. The method was applied to analyses of owl and Bengalese finch vocalizations. [ABSTRACT FROM AUTHOR]

In this paper, we address the affine template matching of general images. The extensive search space of affine transformations necessitates effective searches of the global optimum. The proposed method utilizes differential evolution (DE), which is a method of metaheuristic optimization, to achieve that goal. Self‐adaptive DEs can be useful and are applicable in a wide range of studies as they tune crossover rate and scaling factor (F) themselves over generation iteration. However, this approach is not particularly good for affine template matching because the population often converges to local optima. In order to solve this problem, the population is divided into two equal groups for exploitation and exploration. The former group utilizes current‐to‐best/1, and the latter group adopts improved current‐to‐rand/1 for the mutation scheme. Furthermore, the proportion of the population sizes of the two groups are linearly changed on the basis of the best sum of absolute difference error measurements over each generation. These ideas are easy and simple, but experimental results have revealed our method to be more accurate than the state‐of‐the‐art method. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

For the defect of the traditional vanishing point detection algorithm that is invalid in unstructured environment, a novel vanishing detection algorithm based on Dynamic Template Matching (DTM) is proposed. And a framework of access area recognition is put forward according to the vanishing point line. First, a series of lines are selected from the image in the form of the scanning at the same interval and then calculate the between each line and the previous one. The horizontal position of vanish point is that of the line with the minimum normalized correlation value in all scanning line. Second, a new image is constructed by getting rid of the part above of the viewpoint line, and be divided into several subimages without overlap to extract the multi features. The end, a train set is constructed based on the assumption of no deviation of the vehicle and the test set is classified by multi-kernel learning (MKL) method to obtain passable area. In addition, according to the need of intelligent vehicles during working, a weight-accuracy is delimited by assigning the different weights to the near areas and far areas. This kind of accuracy is more significative than the original one. In the experiments on various environments image sets, the proposed method exhibits favorable performances compared to the other methods. [ABSTRACT FROM AUTHOR]

Highlights • Central airways are a dose-limiting organ-at-risk for stereotactic lung radiotherapy. • The position of the airways is typically not verified during irradiation. • We report a technique for high-frequency markerless 3D airway position monitoring. • It is based on continuous kV imaging, template matching, and triangulation. • It can be implemented on a standard LINAC with no extra hardware. Abstract Background and purpose Central lung stereotactic body radiotherapy (SBRT) can cause proximal bronchial tree (PBT) toxicity. Information on PBT position relative to the high-dose could aid risk management. We investigated template matching + triangulation for high-frequency markerless 3D PBT position monitoring. Materials and methods Kilovoltage projections of a moving phantom (full-fan cone-beam CT [CBCT, 15 frames/second] without MV irradiation: 889 images/dataset + CBCT and 7 frames/second fluoroscopy with MV irradiation) and ten patients undergoing free-breathing stereotactic/hypofractionated lung irradiation (full-fan CBCT without MV irradiation, 470–500 images/dataset) were retrospectively analyzed. 2D PBT reference templates (1 filtered digitally reconstructed radiograph/°) were created from planning CT data. Using normalized cross-correlation, templates were matched to projection images for 2D position. Multiple registrations were triangulated for 3D position. Results For the phantom, 2D right/left PBT position could be determined in 86.6/75.1% of the CBCT dataset without MV irradiation, and 3D position (excluding first 20° due to the minimum triangulation angle) in 84.7/72.7%. With MV irradiation, this was up to 2% less. For right/left PBT, root-mean-square errors of measured versus "known" position were 0.5/0.8, 0.4–0.5/0.7, and 0.4/0.5–0.6 mm for left–right, superior–inferior, and anterior–posterior directions, respectively. 2D PBT position was determined in, on average, 89.8% of each patient dataset (range: 79.4–99.2%), and 3D position (excluding first 20°) in 85.1% (range: 67.9–99.6%). Motion was mainly superior–inferior (range: 4.5–13.6 mm, average: 8.5 mm). Conclusions High-frequency 3D PBT position verification during free-breathing is technically feasible using markerless template matching + triangulation of kilovoltage projection images acquired during gantry rotation. Applications include organ-at-risk position monitoring during central lung SBRT. [ABSTRACT FROM AUTHOR]

In this paper, we present an inverse optimal control-based transfer of motions from human experiments to humanoid robots and apply it to walking in constrained environments. To this end, we introduce a 3D template model, which describes motion on the basis of center-of-mass trajectory, foot trajectories, upper-body orientation, and phase duration. Despite its abstract architecture, with prismatic joints combined with damped series elastic actuators instead of knees, the model (including dynamics and constraints) is suitable for describing both human and humanoid locomotion with appropriate parameters. We present and apply an inverse optimal control approach to identify optimality criteria based on human motion capture experiments. The identified optimal strategy is then transferred to a humanoid robot template model for gait generation by solving an optimal control problem, which takes into account the properties of the robot and differences in the environment. The results of this step are the center-of-mass trajectory, the foot trajectories, the torso orientation, and the single and double support phase durations for a sequence of steps, allowing the humanoid robot to walk within a new environment. In a previous paper, we have already presented one computational cycle (from motion capture data to an optimized robot template motion) for the example of walking over irregular stepping stones with the aim of transferring the motion to two very different humanoid robots (iCub@Heidelberg and HRP-2@LAAS). This study represents an extension, containing an entirely new part on the transfer of the optimized template motion to the iCub robot by means of inverse kinematics in a dynamic simulation environment and also on the real robot. [ABSTRACT FROM AUTHOR]

We present a multilinear statistical model of the human tongue that captures anatomical and tongue pose related shape variations separately. The model is derived from 3D magnetic resonance imaging data of 11 speakers sustaining speech related vocal tract configurations. To extract model parameters, we use a minimally supervised method based on an image segmentation approach and a template fitting technique. Furthermore, we use image denoising to deal with possibly corrupt data, palate surface information reconstruction to handle palatal tongue contacts, and a bootstrap strategy to refine the obtained shapes. Our evaluation shows that, by limiting the degrees of freedom for the anatomical and speech related variations, to 5 and 4, respectively, we obtain a model that can reliably register unknown data while avoiding overfitting effects. Furthermore, we show that it can be used to generate plausible tongue animation by tracking sparse motion capture data. [ABSTRACT FROM AUTHOR]

In object-based image analysis (OBIA), object classification performance is jointly determined by image segmentation, sample or rule setting, and classifiers. Typically, as a crucial step to obtain object primitives, image segmentation quality significantly influences subsequent feature extraction and analyses. By contrast, template matching extracts specific objects from images and prevents shape defects caused by image segmentation. However, creating or editing templates is tedious and sometimes results in incomplete or inaccurate templates. In this study, we combine OBIA and template matching techniques to address these problems and aim for accurate photovoltaic panel (PVP) extraction from very high-resolution (VHR) aerial imagery. The proposed method is based on the previously proposed region–line primitive association framework, in which complementary information between region (segment) and line (straight line) primitives is utilized to achieve a more powerful performance than routine OBIA. Several novel concepts, including the mutual fitting ratio and best-fitting template based on region–line primitive association analyses, are proposed. Automatic template generation and matching method for PVP extraction from VHR imagery are designed for concept and model validation. Results show that the proposed method can successfully extract PVPs without any user-specified matching template or training sample. High user independency and accuracy are the main characteristics of the proposed method in comparison with routine OBIA and template matching techniques. [ABSTRACT FROM AUTHOR]

The three-dimensional (3D) coordinate measurement of radio frequency identification (RFID) multi-tag networks is one of the important issues in the field of RFID, which affects the reading performance of RFID multi-tag networks. In this paper, a novel method for 3D coordinate measurement of RFID multitag networks is proposed. A dual-CCD system (vertical and horizontal cameras) is used to obtain images of RFID multi-tag networks from different angles. The iterative threshold segmentation and the morphological filtering method are used to process the images. The template matching method is respectively used to determine the two-dimensional (2D) coordinate and the vertical coordinate of each tag. After that, the 3D coordinate of each tag is obtained. Finally, a back-propagation (BP) neural network is used to model the nonlinear relationship between the RFID multi-tag network and the corresponding reading distance. The BP neural network can predict the reading distances of unknown tag groups and find out the optimal distribution structure of the tag groups corresponding to the maximum reading distance. In the future work, the corresponding in-depth research on the neural network to adjust the distribution of tags will be done. [ABSTRACT FROM AUTHOR]

To address the scale invariance and the mismatching problems of ORB (oriented FAST and rotated BRIEF), an improved algorithm based on multipurification was put forward and applied in PCB matching and positioning. ORB feature points were initially extracted from both the template image and the target image. Rough matching was then performed by the combination of k-nearest neighbor (k-NN) algorithm and best bin first search algorithm. Considering the existence of a large number of mismatches, the multipurification that consists of neighborhood ratio, bidirectional matching, and cosine similarity was used to purify the set of matching pairs. Finally, the affine matrix between the two images was solved by progressive sample consensus. The experimental results showed that the matching accuracy was significantly improved after the purification by the proposed method. The improved algorithm outperformed original ORB under the condition of image rotation and scaling. The mean rotation angle error was just 1/10th of that of original ORB. The time overhead of the improved algorithm was comparable to that of original ORB, which was about six times faster than that of speeded up robust features and 30 times faster than that of scale-invariant feature transform. The enhanced algorithm demonstrated great advantages in the improvement of accuracy, time overhead, and robustness for the PCB alignment. ©2018 SPIE and IS&T[DOI: 10.1117/1.JEI.27.3.033029] [ABSTRACT FROM AUTHOR]

Image registration is an important task in medical image analysis. Whereas most methods are designed for the registration of two images (pairwise registration), there is an increasing interest in simultaneously aligning more than two images using groupwise registration. Multimodal registration in a groupwise setting remains difficult, due to the lack of generally applicable similarity metrics. In this work, a novel similarity metric for such groupwise registration problems is proposed. The metric calculates the sum of the conditional entropy between each image in the group and a representative template image constructed iteratively using principal component analysis. The proposed metric is validated in extensive experiments on synthetic and intrasubject clinical image data. These experiments showed equivalent or improved registration accuracy compared to other state-of-the-art (dis)similarity metrics and improved transformation consistency compared to pairwise mutual information. [ABSTRACT FROM AUTHOR]

Attenuation correction for positron-emission tomography (PET)/magnetic resonance (MR) hybrid imaging systems and dose planning for MR-based radiation therapy remain challenging due to insufficient high-energy photon attenuation information. We present a novel approach that uses the learned nonlinear local descriptors and feature matching to predict pseudo computed tomography (pCT) images from T1-weighted and T2-weighted magnetic resonance imaging (MRI) data. The nonlinear local descriptors are obtained by projecting the linear descriptors into the nonlinear high-dimensional space using an explicit feature map and low-rank approximation with supervised manifold regularization. The nearest neighbors of each local descriptor in the input MR images are searched in a constrained spatial range of the MR images among the training dataset. Then the pCT patches are estimated through k-nearest neighbor regression. The proposed method for pCT prediction is quantitatively analyzed on a dataset consisting of paired brain MRI and CT images from 13 subjects. Our method generates pCT images with a mean absolute error (MAE) of 75.25 ± 18.05 Hounsfield units, a peak signal-to-noise ratio of 30.87 ± 1.15 dB, a relative MAE of 1.56 ± 0.5% in PET attenuation correction, and a dose relative structure volume difference of 0.055 ± 0.107% in D98\% , as compared with true CT. The experimental results also show that our method outperforms four state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

This paper explains deterministic crowding (DC), introducing the distribution of population for template matching. We apply a simple genetic algorithm (GA) to template matching because this approach is effectively able to optimize geometric transformation parameters, such as parallel transformation, scaling, and in-plane rotation. However, since the simple GA can obtain only one global optimum, detecting multiple objects is difficult. This is not of practical use. In order to detect multiple objects, we focus on DC, which is a multimodal optimization method and able to obtain multiple global and local solutions. In DC, there is a drawback where many individuals converge to one object and, hence, some objects cannot be detected. In order to solve this problem, the proposed method introduces the distribution of population. In experiments, the proposed method, DC, and crowding are applied to template matching and compared. The results confirm that the proposed method is better when an optimal threshold, which is used to create a cluster, is set. [ABSTRACT FROM AUTHOR]

This paper aims to achieve the accurate detection of ball grid array (BGA) solder joints. To this end, the author presented an adaptive template matching method for BGA solder joints based on shape detection. First, the region of interest (ROI) was selected from the X-ray image of the printed circuit board (PCB). Then, an edge template was generated through ROI extraction and threshold segmentation, and the direction vector f the edge template was taken as the prior knowledge. After that, the global traversal search was performed on the image pyramid in the top-down manner, aiming to obtain the potential matching targets. Finally, the edges were adjusted by the least squares method to yield the optimal matching results. The proposed method was proved robust, rapid and accurate through an experiment. The research findings shed new light on the BGA solder joint detection and extraction in various conditions. [ABSTRACT FROM AUTHOR]

We propose a template matching method for the detection of 2D image objects that are characterized by orientation patterns. Our method is based on data representations via orientation scores, which are functions on the space of positions and orientations, and which are obtained via a wavelet-type transform. This new representation allows us to detect orientation patterns in an intuitive and direct way, namely via cross-correlations. Additionally, we propose a generalized linear regression framework for the construction of suitable templates using smoothing splines. Here, it is important to recognize a curved geometry on the position-orientation domain, which we identify with the Lie group SE(2): the roto-translation group. Templates are then optimized in a B-spline basis, and smoothness is defined with respect to the curved geometry. We achieve state-of-the-art results on three different applications: detection of the optic nerve head in the retina (99.83 percent success rate on 1,737 images), of the fovea in the retina (99.32 percent success rate on 1,616 images), and of the pupil in regular camera images (95.86 percent on 1,521 images). The high performance is due to inclusion of both intensity and orientation features with effective geometric priors in the template matching. Moreover, our method is fast due to a cross-correlation based matching approach. [ABSTRACT FROM PUBLISHER]

Although mobile eye-trackers have wide measurement range of gaze, and high flexibility, it is difficult to judge what a subject is actually looking at based only on obtained coordinates, due to the influence of head movement. In this paper, a method to compensate for head movements while seeing the large screen with mobile eye-tracker is proposed, through the use of NIR-LED markers embedded on the screen. The head movements are compensated by performing template matching on the images of view camera to detect the actual eye position on the screen. As a result of the experiment, the detection rate of template matching was 98.6%, the average distance between the actual eye position and the corrected eye position was approximately 16 pixels for the projected image (1920 x 1080). [ABSTRACT FROM AUTHOR]

The template-matching algorithm (TMA) has been widely adopted for improving the reliability of earthquake detection. The TMA is based on calculating the normalized cross-correlation coefficient (NCC) between a collection of selected template waveforms and the continuous waveform recordings of seismic instruments. In realistic applications, the computational cost of the TMA is much higher than that of traditional techniques. In this study, we provide an analysis of the TMA and show how the GPU architecture provides an almost ideal environment for accelerating the TMA and NCC-based pattern recognition algorithms in general. So far, our best-performing GPU code has achieved a speedup factor of more than 800 with respect to a common sequential CPU code. We demonstrate the performance of our GPU code using seismic waveform recordings from the M L 6.6 Meinong earthquake sequence in Taiwan. [ABSTRACT FROM AUTHOR]

Impact craters help scientists to understand the geological history of planetary bodies. The aim of this paper is to improve the existing methodology for impact craters detection in images of planetary surfaces using a new approach based on morphological image processing (MIP). The improved methodology uses MIP followed by template matching based on fast Fourier transform (FFT). In this phase, a probability volume is generated based on the correlation between templates and images. The analysis of this probability volume allows the detection of different size of impact craters. We have applied the improved methodology to detect impact craters in a set of images from Thermal Emission Imaging System onboard the 2001 Mars Odyssey Space probe. The improved methodology has achieved a crater detection rate of 92.23% which can be considered robust, since results were obtained based on geomorphological features, different illumination conditions and low spatial resolution. The achieved results proved the viability of using MIP and template matching by FFT, to detect impact craters from planetary surfaces. [ABSTRACT FROM AUTHOR]

Template matching is the process of accurately extracting the interesting regions in a source image according to reference templates. In this paper, the gravitational search algorithm (GSA) is employed as a novel search strategy for template matching. However, the basic GSA is easily trapped in a local optimum and has a poor exploitation ability. In this paper, to enhance the optimization performance of GSA, a novel cross-search strategy based on chaotic global search (CGS) and cloud local search (CLS) is incorporated into GSA. The new variant is named chaotic cloud GSA (CCGSA). CGS makes full use of the ergodicity of chaos theory to improve global search ability and to avoid premature convergence. Inspired by the randomness and stable tendency of the normal cloud model, CLS was formed to realize a refined exploitation in the neighborhood of the current best solution; therefore, it can enhance optimization efficiency. Comparative experiments on six composite benchmark functions indicate that CCGSA convergence performance is superior to that of two advanced variants of GSA. Moreover, when applied to template matching, CCGSA performs better than the other selected intelligent optimization algorithms. [ABSTRACT FROM AUTHOR]

SUMMARY It is well known that points on a plane in 3D world are related to corresponding image points in a view of a moving camera by projective translation. Good image features have robust projectivity under any camera movements. In the standard performance evaluation of image processing, real captured images of a scene are used ordinarily. However, it is not enough to evaluate in detail because the variation of camera angle and distance to target objects are limited and the capturing cost is expensive. During the early stage of the image processing development, the basic performance measurement should be the most important in an easy way. We propose a projectivity diagnosis method to measure the performance of local descriptor base template matching between a template image and reference images which are created by deforming the template image. This template matching consists of a feature image point extraction and a local descriptor matching. The proposed method evaluates the positional accuracy of the extracted feature points and the matching with local descriptor. Four metrics are introduced to evaluate the projectivity of template matching. In the experiment, our proposed diagnosis method expose the projectivity of SIFT, SURF, and ORB. SIFT showed the better robustness than the others. [ABSTRACT FROM AUTHOR]

During the testing and sorting of LED chips, traditional methods do not exclude the polycrystalline and fragmentary LED chips from the normal chips well. The purpose of this paper is to propose a new algorithm to solve this problem. The algorithm consists of three steps. Firstly, present a simple but efficient image segmentation method to get blobs. Secondly, analyze the blobs to exclude abnormal blobs and predict the pose (position and orientation) of the potential object based on the pose of the minimum enclosing rectangle (MER) of each remained blob. Finally, according to the predicted poses, locate the LED chips precisely in the originally captured image based on gradient orientation features. Experiments show that the algorithm is not only robust to illumination variation but also can locate the LED chips and exclude the polycrystalline and fragmentary chips efficiently. [ABSTRACT FROM AUTHOR]

We propose a novel palm vein recognition scheme based on an adaptive 2D Gabor filter. Three key steps were studied in this scheme: region of interest (ROI) extraction, adaptive Gabor filtering, and template matching. First, in the palm vein image extraction step, the authors used the index finger on both sides of the valley to locate the square area, and then iteratively expanded the area of the square box to maximise the ROI. Second, in the feature extraction step, a novel parameter selection scheme was proposed for optimising the Gabor filter. Third, in the template matching step, the author presented a novel template matching algorithm referred to as the minimum normalised Hamming distance. Experimental results demonstrated that the scheme achieved good performance with an EER of 0.12%. [ABSTRACT FROM AUTHOR]

Since most existing saliency detection models are not suitable for the condition that the salient objects are near at the image border, the authors propose a saliency detection approach based on adaptive background template (SCB) despite of the position of the salient objects. First, a selection strategy is presented to establish the adaptive background template by removing the potential saliency superpixels from the image border regions, and the initial saliency map is obtained. Second, a propagation mechanism based on K-means algorithm is designed for maintaining the neighbourhood coherence of the above saliency map. Finally, a new spatial prior is presented to integrate the saliency detection results by aggregating two complementary measures such as image centre preference and the background template exclusion. Comprehensive evaluations on six benchmark datasets indicate that the authors' method outperforms other state-of-the-art approaches. In addition, a new dataset containing 300 challenging images is constructed for evaluating the performance of various salient object detection methods. [ABSTRACT FROM AUTHOR]

This paper used dynamic time warping( DTW) algorithm to align side channel signals in differential power analysis( DPA) attacks. Based on the principle of DTW algorithm which produced a warp path for recomposing signal traces pair,it proposed a flexible alignment method. The practical experiments based on simulated misalignment signals obtained from a microcontroller( AT89C52) which implemented the data encryption standard( DES) cipher in software and inserted the random time delays. As the results show,running DPA on misaligned traces requires a much larger trace set when misalignment is appreciably increased,in contrast,performing DPA on the traces modified by flexible alignment can make it very low-cost in terms of the number of traces that need to be processed. The result also confirms the effectiveness of method mentioned above. [ABSTRACT FROM AUTHOR]

Template matching is a basic and crucial process for image processing. In this paper, a hybrid method of stochastic fractal search (SFS) and lateral inhibition (LI) is proposed to solve complicated template matching problems. The proposed template matching technique is called LI-SFS. SFS is a new metaheuristic algorithm inspired by random fractals. Furthermore, lateral inhibition mechanism has been verified to have good effects on image edge extraction and image enhancement. In this work, lateral inhibition is employed for image preprocessing. LI-SFS takes both the advantages of SFS and lateral inhibition which leads to better performance. Our simulation results show that LI-SFS is more effective and robust for this template matching mission than other algorithms based on LI. [ABSTRACT FROM AUTHOR]

This study proposes a template matching simplification method from the perspective of shape cognition based on the typical template characteristics of building distributions and representations. The method first formulates a series of templates to abstract the building shape by generalizing their polygons and analyzing their symbolic meanings, then conducts the simplification by searching and matching the most similar template that can be used later to replace the original building. On the premise of satisfying the individual geometric accuracy on a smaller scale, the proposed method can enhance the impression of well-known landmarks and reflect the pattern in mapping areas by the symbolic template. The turning function that describes shape by measuring the changes of the tangent-angle as a function of the arc-length is employed to obtain the similar distance between buildings and template polygons, and the least squares model is used to control the geometry matching of the candidate template. Experiments on real datasets are carried out to assess the usefulness of this method and compare it with two existing methods. The experiments suggest that our method can preserve the main structure of building shapes and geometric accuracy. [ABSTRACT FROM AUTHOR]

The head pose estimation technique predicts the rotation of the human head by analyzing a person's face in a digital image. The head pose estimation framework uses two processes for the estimation. The first step is the detection of the face and facial features using a Haar-like feature detector. Methods proposed in previous studies generally provided a low overall detection ratio of each facial feature. Therefore, the pre-processing step for storing the facial features as a template could be time consuming. We propose a calibration method that finds one eye feature that cannot be found on the front part of the face. The method was evaluated by conducting an experiment to measure the detection accuracy of the face and facial features. The second process is used for the template-matching algorithm while the facial features are being tracked. As the experiment proceeded, we measured the time required to execute the estimation on an Android device. The head pose estimation procedure uses the coordinates of facial features. The algorithms used in the proposed systems show that the detection and tracking processes require approximately 230 ms and 20 ms, respectively. In addition, the calibration method proved to be effective in terms of decreasing the detection failure rate by approximately 8 %. Thus, this result confirms the effectiveness of our method on mobile devices. [ABSTRACT FROM AUTHOR]

This article discusses the challenges associated with motion tracking of insects. The authors comment on the various methods of insect tracking, including studies of bees, ants, and spiders. They examine the development of tracking algorithms to detect insect movement, including methods such as optical flow, color matching, and template matching.

In spite of the advantages of biometrics as an identity verification technology, some concerns have been raised due to the high sensitivity of biometric data: any information leakage poses a severe privacy threat. To solve those issues only protected templates should be stored or exchanged for recognition purposes. In order to improve the performance and achieve more secure and privacy-preserving systems, we propose a general framework for multi-biometric template protection based on homomorphic probabilistic encryption, where only encrypted data is handled. Three fusion levels are thoroughly analysed, showing that all requirements described in the ISO/IEC 24745 standard on biometric data protection are met with no accuracy degradation. Furthermore, even if all the process is carried out in the encrypted domain, no encryptions are necessary during verification, thereby allowing an efficient verification which can be deployed for real-time applications. Finally, experiments are carried out on a reproducible research framework. The results obtained show high accuracy rates, reaching EERs as low as 0.12%, and requiring protected templates comprising 200 KB. [ABSTRACT FROM AUTHOR]

The Shape-from-Template (SfT) problem is to recover the 3D shape of a deformable object from a single image, given a 3D template and a deformation constraint. We propose Particle-SfT, a new SfT algorithm which handles isometric and non-isometric deformations. We build Particle-SfT upon a particle system guided by deformation and reprojection constraint projections. Reconstruction is achieved by evolving particles to a globally attractive equilibrium, while taking observable external forces such as gravity into account, if any. Particle-SfT may be used to refine an existing initial shape. However, in practice we simply use the template as initial guess. This is because, as opposed to the existing refining methods, Particle-SfT has an extremely wide convergence basin. Particle-SfT is also faster than the existing refining methods. This is because it moves pieces of the shape's mesh independently to achieve larger step size by optimal constraint projection. We proved its convergence to a fixed-point. We experimented it with synthetic and real data. It has the same accuracy as the best performing isometric method and consistently outperforms all existing elastic methods in almost all cases, while being much faster. [ABSTRACT FROM AUTHOR]

Moving object tracking under complex scenes remains to be a challenging problem because the appearance of a target object can be drastically changed due to several factors, such as occlusions, illumination, pose, scale change and deformation. This study proposes an adaptive multi–feature fusion strategy, in which the target appearance is modeled based on timed motion history image with HSV color histogram features and edge orientation histogram features. The variances based on the similarities between the candidate patches and the target templates are used for adaptively adjusting the weight of each feature. Double templates matching, including online and offline template matching, is adopted to locate the target object in the next frame. Experimental evaluations on challenging sequences demonstrate the accuracy and robustness of the proposed algorithm in comparison with several state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Image registration is a fundamental task in image processing used to match two different images of same object acquired under different situations and at different times. Implementation of image registration can be intensity value based or feature based. Implementing intensity based image registration technique with reduced time delay on embedded platform DevKit-8500D is presented in this paper. DevKit-8500D is used due to its compatibility for higher programming language such as C/C++ and hardware availability of on-board DSP making it adequate for image and video processing. Template matching on resized images by computation of cross-correlation is used for registration purpose to reduce the computational delay of whole process. A comparison between normal and resized images is also presented to show errors produced in results due to resizing operation and to emphasize on reduced amount of time-delay. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid method of grey wolf optimizer (GWO) and lateral inhibition (LI) is proposed to solve complicated template matching problems. The proposed template matching technique is called LI-GWO. GWO is a new meta-heuristic algorithm inspired by the hunting behavior and social leadership of grey wolves in nature. In addition, lateral inhibition mechanism has been verified to have good effects on image edge extraction and image enhancement. So we employ lateral inhibition for image pre-processing. LI-GWO combines both advantages of GWO and literal inhibition and makes better performance. Series of comparative experimental results show that the proposed method achieves the best balance in comparison to other algorithms based on lateral inhibition in terms of estimation accuracy and the computational cost. [ABSTRACT FROM AUTHOR]

Fast-Match is a fast algorithm for approximate template matching under 2D affine transformations that minimizes the Sum-of-Absolute-Differences (SAD) error measure. There is a huge number of transformations to consider but we prove that they can be sampled using a density that depends on the smoothness of the image. For each potential transformation, we approximate the SAD error using a sublinear algorithm that randomly examines only a small number of pixels. We further accelerate the algorithm using a branch-and-bound-like scheme. As images are known to be piecewise smooth, the result is a practical affine template matching algorithm with approximation guarantees, that takes a few seconds to run on a standard machine. We perform several experiments on three different datasets, and report very good results. [ABSTRACT FROM AUTHOR]

For cryptographic algorithms, secret keys should be generated randomly as the security of the system depends on the key and therefore generation of random sequences is vital. Randomness testing is done by means of statistical randomness tests. In this work, we show that the probabilities for the overlapping template matching test in the NIST test suite are only valid for a specific template and need to be recalculated for the other templates. We calculate the exact distribution for all 4-bit templates and propose new randomness tests, namely template matching tests. The new tests can be applied to any sequence of minimum length 5504 whereas the overlapping template matching test in the NIST test suite can only be applied to sequences of minimum length 106. Moreover, we apply the proposed tests to biased nonrandom data and observe that the new tests detect the nonrandom behavior of the generator even for a bias of 0:001, whereas the template matching tests in NIST cannot detect that bias. [ABSTRACT FROM AUTHOR]

Digital technology and the widespread use of the Internet has increased the speeds at which digital data can be obtained and shared in daily life. In parallel to this, there are important concerns regarding the confidentiality of private data during data transmissions and the possibility that data might fall into the hands of third parties. Issues relating to data safety can also affect patients' medical images and other information relating to these images. In this study, we propose a new method based on block matching that can be used to hide the patient information in medical images. In this method, 8 scanning orders (6 of which are newly designed) are developed to provide high image quality. By diversifying the number of scanning orders, we aim to achieve the lowest number of bit changes. The performance of the developed method is measured using the number of bits subject to change, the peak signal-to-noise ratio and the mean structural similarity index measure image quality assessment metrics, and steganalysis attacks. The method we developed was found to be more effective in hiding data compared to the classical least significant bit method. [ABSTRACT FROM AUTHOR]

A fast normalized cross correlation (FNCC) based machine vision algorithm was proposed in this study to develop a method for detecting and counting immature green citrus fruit using outdoor colour images toward the development of an early yield mapping system. As a template matching method, FNCC was used to detect potential fruit areas in the image, which was the very basis for subsequent false positive removal. Multiple features, including colour, shape and texture features, were combined in this algorithm to remove false positives. Circular Hough transform (CHT) was used to detect circles from images after background removal based on colour components. After building disks centred in centroids resulted from both FNCC and CHT, the detection results were merged based on the size and Euclidian distance of the intersection areas of the disks from these two methods. Finally, the number of fruit was determined after false positive removal using texture features. For a validation dataset of 59 images, 84.4 % of the fruits were successfully detected, which indicated the potential of the proposed method toward the development of an early yield mapping system. [ABSTRACT FROM AUTHOR]

Template matching (TM) was proposed in the literature a decade ago to efficiently remove non-local redundancies within an image without transmitting any overhead of displacement vectors. However, the large computational complexity introduced at both the encoder and the decoder, especially for a large search range, limits its widespread use. This paper proposes a hash-based line-by-line template matching (hLTM) for lossless screen image coding, where the non-local redundancy commonly exists in text and graphics parts. By hash-based search, it can largely reduce the search complexity of template matching without an accuracy degradation. Besides, the line-by-line template matching increases prediction accuracy by using a fine granularity. Experimental results show that the hLTM can significantly reduce both the encoding and decoding complexities by 68 and 23 times, respectively, compared with the traditional TM with a search radius of 128. Moreover, when compared with High Efficiency Video Coding screen content coding test model SCM-1.0, it can largely improve coding efficiency by up to 12.68% bits saving on screen contents with rich texts/graphics. [ABSTRACT FROM PUBLISHER]