An automatic timing detection method using word spotting and dynamic programming for superimposing captions in television programs

Flexible Sequence Matching technique: An effective learning-free approach for word spotting

Coronal loops are bright, filamentary structures formed by thermal plasmas constrained by the sun’s magnetic field. Studying coronal loops provides insights into magnetic fields and their role in coronal heating processes. We propose a new automatic coronal loop detection method to optimize the problem of existing algorithms in detecting low-intensity coronal loops. Our method employs a line-Gaussian filter to enhance the contrast between coronal loops and background pixels, facilitating the detection of low-intensity ones. Following the detection of coronal loops, each loop is extracted using a method based on approximate local direction. Compared with the classical automatic detection method, Oriented Coronal Curved Loop Tracing (OCCULT), and its improved version, OCCULT-2, the proposed method demonstrates superior accuracy and completeness in loop detection. Furthermore, testing with images from the Transition Region and Coronal Explorer (TRACE) at 173 Å, the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO) at 193 Å, and the High-Resolution Coronal Imager (Hi-C) at 193 Å and 172 Å confirms the robust generalization capabilities of our method. Statistical analysis of the cross-section width of coronal loops shows that most of the loop widths are resolved in Hi-C images.

There have been many published works on using Dynamic Time Warping (DTW) for word image matching. It has also been shown that constrained DTW works better than classical DTW, due to it's property to limit the warping path and to avoid pathological matching. Moreover, some studies also demonstrate that variants of DTW can perform better, compared to classical DTW. To take advantages from these techniques, we propose in this paper to study how combination of such approaches can even more improve the results. First, we propose to use pseudo Local DTW (LDTW: more efficient than DTW in our experiments) with a constraint band. Next, several other sequence matching techniques are parametrically combined to study their potential advantages. For this, we adapted the approach to the context of word image matching to obtain the value of the parameters. It is shown that, at least on Bentham and George Washington datasets, results can be improved by combining some algorithms. Please note that the goal of this research work is not to propose a competitive word spotting technique but to propose a dynamic programming based learning free approach for word spotting or image matching, which can perform moderately with respect to other existing approaches in this domain.

Distributed systems have been widely used in the information technology industry. However, with the increasing scale and complexity of distributed systems, the efficiency and accuracy of manual anomaly detection in system logs have decreased. Therefore, there is a great demand for a highly accurate and efficient automatic anomaly detection method based on system log analysis to ensure the reliability and the stability of large-scale distributed systems. In this paper, we propose VeLog, an automatic anomaly detection method based on variational autoencoders (VAEs). In the offline training phase, VeLog learns the patterns of normal log sequences and then generates normal intervals. In the online detection phase, VeLog detects an anomaly by automatically evaluating whether the distance between the input vector and its estimated vector matches these normal intervals. We evaluate VeLog on log datasets collected from representative distributed systems. The experimental results demonstrate that VeLog can detect anomalies with high accuracy and good efficiency.

In order to reduce the probability of website users being attacked and maintain the safety of website operation, this study proposes an automatic vulnerability detection method of websites based on associated data. We use plug-ins to scan the website in all directions, establish a scanning database, and classify and store the scanned web data. By applying optimized an a priori association rule algorithm, key features are extracted from web scan data, which are then transformed into input samples for a K-means clustering algorithm. The aim is to efficiently extract feature attributes of website vulnerability data and ultimately construct a text vectorized representation of vulnerability data. Convolutional neural networks can automatically detect website vulnerabilities by using the constructed text vector as input. Experimental verification shows that this method demonstrates comprehensive data coverage, efficient processing speed, and high-precision recognition performance. It not only significantly reduces the clustering analysis time, but also ensures the accuracy and timeliness of vulnerability detection.

Feature-based NC machining, which requires high quality of 3D CAD model, is widely used in machining aircraft structural part. However, there has been little research on how to automatically detect the CAD model errors. As a result, the user has to manually check the errors with great effort before NC programming. This paper proposes an automatic CAD model errors detection approach for aircraft structural part. First, the base faces are identified based on the reference directions corresponding to machining coordinate systems. Then, the CAD models are partitioned into multiple local regions based on the base faces. Finally, the CAD model error types are evaluated based on the heuristic rules. A prototype system based on CATIA has been developed to verify the effectiveness of the proposed approach. Highlights The 3D CAD model of an aircraft structural part is partitioned into multiple local regions in terms of the base faces. The fillet association level graph is proposed to represent the local region for exploring the CAD model errors. An automatic CAD model error detection method based on heuristics rule is proposed.

News currently spreads rapidly through the internet. Because fake news stories are designed to attract readers, they tend to spread faster. For most readers, detecting fake news can be challenging and such readers usually end up believing that the fake news story is fact. Because fake news can be socially problematic, a model that automatically detects such fake news is required. In this paper, we focus on data-driven automatic fake news detection methods. We first apply the Bidirectional Encoder Representations from Transformers model (BERT) model to detect fake news by analyzing the relationship between the headline and the body text of news. To further improve performance, additional news data are gathered and used to pre-train this model. We determine that the deep-contextualizing nature of BERT is best suited for this task and improves the 0.14 F-score over older state-of-the-art models.

Security of Internet-of-Things (IoT) systems is important due to their widespread usage in everyday life. Much research has been performed on analyzing the security of IoT communication protocols and operating systems. However, few studies have focused on analyzing the security of IoT applications and automatic detection of vulnerabilities in them. In these studies, the code of IoT applications and operating systems are analyzed statically to detect vulnerabilities. To the best of our knowledge, there is no dynamic analysis solution suggested for vulnerability detection in such applications, although this method is more accurate than static analysis. In fact, IoT applications are executed in special-purpose hardware, which makes their dynamic analysis more difficult than ordinary applications. In this paper, we propose a technical solution that combines static and dynamic analysis methods to automatically detect vulnerability in applications of Tizen IoT operating system. We consider Native and Web Tizen applications and present an automatic vulnerability detection method for each type of application. Our focus is on detecting buffer overflow and XSS vulnerability classes in Native and Web applications, respectively. We have evaluated the effectiveness of our method using a group of native and web test programs. The results of our experiments show that our solution is able to detect the vulnerability in these programs effectively.

Long term exposure of photovoltaic (PV) systems under relatively harsh and changing environmental conditions can result in fault conditions developing during the operational lifetime. The present solution is for system operators to manually perform condition monitoring of the PV system. However, it is time-consuming, inaccurate and dangerous. Thus, automatic fault detection and diagnosis is a critical task to ensure the reliability and safety in PV systems. The current state-of-the-art techniques either cannot provide enough detailed fault information with high accuracy or have too much complexity. This work presents an automatic fault detection and diagnosis method for string based PV systems. It combines an artificial neural network (ANN) with the conventional analytical method to conduct the fault detection and diagnosis tasks. A two-layered ANN is applied to predict the expected power which is then compared with the measured power from the real PV system. Based on the difference between the ANN estimated power and the measured power, the open circuit voltage and short circuit current of the PV string determined using analytical equations are used to identify any of the six defined fault types. The proposed method has a fast detection, compact structure and good accuracy. Simulation results show the effective fault detection and diagnosis capability of the proposed method.

Automatic target detection methods for synthetic aperture radar (SAR) images are sensitive to image resolution, size of the target to be detected, clutter complexity, and speckle noise level. A robust automatic target detection method needs to be less sensitive to the above factors. In this study, a constant false alarm rate (CFAR) based automatic target detection method which can find a target and its heterogeneous clutter independent of the image resolution and the target size has been developed. The proposed method provides efficient memory usage and low computational complexity.

The result of automatic traffic-congestion detection method in bad weather is inaccurate. In response to this situation, we propose a detection method of traffic congestion based on histogram equalisation and discrete-frame difference. This method uses discrete-frame difference algorithm to extract the images that have vehicle information firstly. Then, the method employs histogram equalisation algorithm to eliminate the noise of the vehicle images. We also propose a corresponding traffic congestion index statistical algorithm in this step. After that, this method recognises vehicles from the video and computes the traffic congestion index. Finally, the method transforms the dimension of traffic congestion index and obtains the state of traffic congestion. It is proved by experiments that the method increases the accuracy rate of automatic traffic congestion detection in bad weather. This method has a wonderful prospect in some areas where there is usually bad weather.

Previous work in literature has indicated that template of web pages represent noisy information in web collections, and advocate that the simple removal of template result in improvements in quality of results provided by Web search systems. In this paper, we study the impact of template removal in two distinct scenarios: large scale web search collections, which consist of several distinct websites, and intrasite web collections, involving searches inside of web sites. Our work is the first in literature to study the impact of template removal to search systems in large scale Web collections. The study was carried out using an automatic template detection method previously proposed by us. As contributions, we present statistics about the application of this automatic template detection method to the well known GOV2 reference collection, a large scale Web collection. We also present experiments comparing the amount of template detected by our automatic method to the ones obtained when humans select templates. And finally, experiments which indicate that, in both experimented scenarios, template removal does not improve the quality of results provided by search systems, but can play the role of an effective loss compression method by reducing the size of their indexes.

Bai, Y., 2019. Target detection method of underwater moving image based on optical flow characteristics. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 668–673. Coconut Creek (Florida), ISSN 0749-0208.The detection and recognition ability of underwater moving image target is improved, and an automatic target detection method based on optical flow feature is proposed. The image acquisition model of underwater moving image target detection is constructed, and the local information entropy feature of underwater moving target image is detected by optical flow field scale decomposition method. The contrast feature of the distribution area of the optical flow field of the underwater moving image is extracted, and the information fusion model of the underwater moving target image is constructed according to the statistical value of the inter-frame difference of the underwater moving image. The center position of the candidate target is obtained, and the adaptive feature extraction of the underwater moving image target is carried out by using the correlation fusion method between the pixel neighborhoods, and the automatic detection of the underwater moving image target is realized according to the optical flow feature distribution. The simulation results show that the proposed method has good adaptability, high accuracy and strong anti-optical flow interference ability in underwater moving image target recognition.

It is important to measure the spatial performance of an in-flight satellite sensor to determine if any spatial resolution degradations occur over time; then some compensation methods can be adopted to improve the image quality. In satellite on-orbit measurements of spatial resolution, the performance of the sensors can be objectively evaluated through the point spread function (PSF) or modulation transfer function (MTF). The well-known image-based PSF derivation method of sampling along a slanted linear step structure from the degraded image is widely used in the satellite image restoration area. Due to the non-uniformity of the linear ground objects, generally, the operators have to pick out good profiles from all the profiles along the edge in order to increase the estimation accuracy. However, only some qualitative criteria have been proposed in the literature to instruct researchers how to pick out good profiles. In this article, we propose a method to quantitatively measure the homogeneity and contrast of the candidate linear structures in order to extract the optimal profiles. This method first automatically detects and locates the slanted linear step edge object in the image. Then it measures the homogeneity and the contrast at each location along the edge by using two adjacent square regions on both sides of the linear step structure. The locations that have high measurement values are regarded as the optimal locations. Finally, the optimal profiles can be extracted at these edge locations to derive an accurate PSF estimation by differentiation. This method makes an automatic optimal step edge detection method for PSF derivation to be practical. Experiment results on simulated and real images are provided. This article shows that using only the ‘optimal’ profiles can play a real and important role and can achieve a good PSF estimation result.

The existence of clouds has seriously affected the application of remote sensing data. Therefore, accurate cloud detection is of great significance in remote sensing image processing and application. Traditional cloud detection methods are complex to operate and often require the additional ancillary information. An automatic cloud detection method based on convolutional neural network (CNN) is proposed in this study. The method utilizes a convolutional network structure to classify training samples for cloud and non-cloud. In order to make full use of image information, images of different band numbers are applied to evaluate the influence of the spectrum on cloud detection. Experiments and verification on Landsat 8 images show that the proposed method based on CNN can comprehensively and automatically detect different types of clouds on different surface types, and the cloud detection result using 7 bands is the optimal. The algorithm takes full advantage of image information and does not rely on thermal infrared information, which has practical application value for improving image utilization and subsequent retrieval of remote sensing parameters.