scholarly journals An Automated Approach for Electric Network Frequency Estimation in Static and Non-Static Digital Video Recordings

2021 ◽  
Vol 7 (10) ◽  
pp. 202
Author(s):  
Georgios Karantaidis ◽  
Constantine Kotropoulos
Keyword(s):  
Digital Video ◽  
State Of The Art ◽  
Frequency Estimation ◽  
Real Life ◽  
Ground Truth ◽  
Video Frame ◽  
Indoor Environments ◽  
Electric Network ◽  
Video Recordings ◽  
Different Levels

Electric Network Frequency (ENF) is embedded in multimedia recordings if the recordings are captured with a device connected to power mains or placed near the power mains. It is exploited as a tool for multimedia authentication. ENF fluctuates stochastically around its nominal frequency at 50/60 Hz. In indoor environments, luminance variations captured by video recordings can also be exploited for ENF estimation. However, the various textures and different levels of shadow and luminance hinder ENF estimation in static and non-static video, making it a non-trivial problem. To address this problem, a novel automated approach is proposed for ENF estimation in static and non-static digital video recordings. The proposed approach is based on the exploitation of areas with similar characteristics in each video frame. These areas, called superpixels, have a mean intensity that exceeds a specific threshold. The performance of the proposed approach is tested on various videos of real-life scenarios that resemble surveillance from security cameras. These videos are of escalating difficulty and span recordings from static ones to recordings, which exhibit continuous motion. The maximum correlation coefficient is employed to measure the accuracy of ENF estimation against the ground truth signal. Experimental results show that the proposed approach improves ENF estimation against the state-of-the-art, yielding statistically significant accuracy improvements.

scholarly journals Learning from Weak-Label Data: A Deep Forest Expedition

2020 ◽  
Vol 34 (04) ◽  
pp. 6251-6258
Author(s):  
Qian-Wei Wang ◽  
Liang Yang ◽  
Yu-Feng Li
Keyword(s):  
Cross Validation ◽  
State Of The Art ◽  
Frequency Estimation ◽  
Ground Truth ◽  
Layer By Layer ◽  
Label Data ◽  
Information Layer ◽  
Deep Forest ◽  
Class Labels ◽  
Number Of Classes

Weak-label learning deals with the problem where each training example is associated with multiple ground-truth labels simultaneously but only partially provided. This circumstance is frequently encountered when the number of classes is very large or when there exists a large ambiguity between class labels, and significantly influences the performance of multi-label learning. In this paper, we propose LCForest, which is the first tree ensemble based deep learning method for weak-label learning. Rather than formulating the problem as a regularized framework, we employ the recently proposed cascade forest structure, which processes information layer-by-layer, and endow it with the ability of exploiting from weak-label data by a concise and highly efficient label complement structure. Specifically, in each layer, the label vector of each instance from testing-fold is modified with the predictions of random forests trained with the corresponding training-fold. Since the ground-truth label matrix is inaccessible, we can not estimate the performance via cross-validation directly. In order to control the growth of cascade forest, we adopt label frequency estimation and the complement flag mechanism. Experiments show that the proposed LCForest method compares favorably against the existing state-of-the-art multi-label and weak-label learning methods.

scholarly journals Exploitation of deep learning in the automatic detection of cracks on paved roads

GEOMATICA ◽  
2019 ◽  
Vol 73 (2) ◽  
pp. 29-44
Author(s):  
Won Mo Jung ◽  
Faizaan Naveed ◽  
Baoxin Hu ◽  
Jianguo Wang ◽  
Ningyuan Li
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Ground Truth ◽  
Learning Networks ◽  
Test Image ◽  
Convolutional Network ◽  
Image Patches ◽  
Severity Levels ◽  
First Time ◽  
Different Levels

With the advance of deep learning networks, their applications in the assessment of pavement conditions are gaining more attention. A convolutional neural network (CNN) is the most commonly used network in image classification. In terms of pavement assessment, most existing CNNs are designed to only distinguish between cracks and non-cracks. Few networks classify cracks in different levels of severity. Information on the severity of pavement cracks is critical for pavement repair services. In this study, the state-of-the-art CNN used in the detection of pavement cracks was improved to localize the cracks and identify their distress levels based on three categories (low, medium, and high). In addition, a fully convolutional network (FCN) was, for the first time, utilized in the detection of pavement cracks. These designed architectures were validated using the data acquired on four highways in Ontario, Canada, and compared with the ground truth that was provided by the Ministry of Transportation of Ontario (MTO). The results showed that with the improved CNN, the prediction precision on a series of test image patches were 72.9%, 73.9%, and 73.1% for cracks with the severity levels of low, medium, and high, respectively. The precision for the FCN was tested on whole pavement images, resulting in 62.8%, 63.3%, and 66.4%, respectively, for cracks with the severity levels of low, medium, and high. It is worth mentioning that the ground truth contained some uncertainties, which partially contributed to the relatively low precision.

scholarly journals Rethinking the Bottom-Up Framework for Query-Based Video Localization

2020 ◽  
Vol 34 (07) ◽  
pp. 10551-10558 ◽  
Author(s):  
Long Chen ◽  
Chujie Lu ◽  
Siliang Tang ◽  
Jun Xiao ◽  
Dong Zhang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Video Frame ◽  
Top Down ◽  
Bottom Up ◽  
Multi Scale ◽  
Feature Pyramid ◽  
Multi Level ◽  
Classification And Regression ◽  
Model Graph

In this paper, we focus on the task query-based video localization, i.e., localizing a query in a long and untrimmed video. The prevailing solutions for this problem can be grouped into two categories: i) Top-down approach: It pre-cuts the video into a set of moment candidates, then it does classification and regression for each candidate; ii) Bottom-up approach: It injects the whole query content into each video frame, then it predicts the probabilities of each frame as a ground truth segment boundary (i.e., start or end). Both two frameworks have respective shortcomings: the top-down models suffer from heavy computations and they are sensitive to the heuristic rules, while the performance of bottom-up models is behind the performance of top-down counterpart thus far. However, we argue that the performance of bottom-up framework is severely underestimated by current unreasonable designs, including both the backbone and head network. To this end, we design a novel bottom-up model: Graph-FPN with Dense Predictions (GDP). For the backbone, GDP firstly generates a frame feature pyramid to capture multi-level semantics, then it utilizes graph convolution to encode the plentiful scene relationships, which incidentally mitigates the semantic gaps in the multi-scale feature pyramid. For the head network, GDP regards all frames falling in the ground truth segment as the foreground, and each foreground frame regresses the unique distances from its location to bi-directional boundaries. Extensive experiments on two challenging query-based video localization tasks (natural language video localization and video relocalization), involving four challenging benchmarks (TACoS, Charades-STA, ActivityNet Captions, and Activity-VRL), have shown that GDP surpasses the state-of-the-art top-down models.

scholarly journals Competency Based Education – Current Global Practices

2017 ◽  
Author(s):  
Marcela Hernández-de-Menéndez ◽  
Ruben Morales-Menendez
Keyword(s):  
Lifelong Learning ◽  
State Of The Art ◽  
Supply And Demand ◽  
Real Life ◽  
Competency Assessment ◽  
Competency Based Education ◽  
Assessment Techniques ◽  
Competency Based ◽  
Competency Level ◽  
Different Levels

Competency Based Education (CBE) is considered an alternative to face the lack of individuals with the appropriate labour abilities. A state of the art on CBE in terms of the practices being performed by main worldwide universities/colleges is presented. Main promoted competencies include effective communication, critical thinking and lifelong learning. Also,  teaching and practice activities are determined such as real life situations and simulations. Regarding competency assessment techniques, a mix of them is used to guarantee the desired competency level. Achievements of competencies are reported with a pass or not pass grade and with narrative transcripts. CBE benefits from student's perspective are also determined. The main advantage of CBE is that measures what a student can do after completing a program. It is also flexible, as universities/colleges of any size/age can incorporate it at different levels, which depends on their resources and strategies. Even though CBE has proven to solve a global problem, the gap between the supply and demand of skillful people can only be reduced if all the concerned parties work together in a coordinated manner.  

scholarly journals Quality assessment for 3D reconstruction of building interiors

2020 ◽  
Vol 2020 (17) ◽  
pp. 36-1-36-7
Author(s):  
Umamaheswaran RAMAN KUMAR ◽  
Inge COUDRON ◽  
Steven PUTTEMANS ◽  
Patrick VANDEWALLE
Keyword(s):  
3D Reconstruction ◽  
Real World ◽  
State Of The Art ◽  
Ground Truth ◽  
3D Models ◽  
Indoor Environments ◽  
Ground Truth Data ◽  
Complex Design ◽  
Reconstructed Model

Applications ranging from simple visualization to complex design require 3D models of indoor environments. This has given rise to advancements in the field of automated reconstruction of such models. In this paper, we review several state-of-the-art metrics proposed for geometric comparison of 3D models of building interiors. We evaluate their performance on a real-world dataset and propose one tailored metric which can be used to assess the quality of the reconstructed model. In addition, the proposed metric can also be easily visualized to highlight the regions or structures where the reconstruction failed. To demonstrate the versatility of the proposed metric we conducted experiments on various interior models by comparison with ground truth data created by expert Blender artists. The results of the experiments were then used to improve the reconstruction pipeline.

scholarly journals Comparison of Different Hardware Configurations for 2D SLAM Techniques Based on Google Cartographer Use Case

2021 ◽  
Author(s):  
Łukasz Sobczak ◽  
Katarzyna Filus ◽  
Joanna Domańska ◽  
Adam Domański
Keyword(s):  
Real Life ◽  
Ground Truth ◽  
Cost Effective ◽  
Global Navigation Satellite Systems ◽  
Use Case ◽  
Indoor Environments ◽  
Satellite Systems ◽  
Localization And Mapping ◽  
Hardware Configuration ◽  
Global Navigation Satellite

Abstract One of the most challenging topics in Robotics is Simultaneous Localization and Mapping (SLAM) in the indoor environments. Due to the fact that Global Navigation Satellite Systems cannot be successfully used in such environments, different data sources are used for this purpose, among others LiDARs (Light Detection and Ranging), which have advanced from numerous other technologies. Other embedded sensors can be used along with LiDARs to improve SLAM accuracy, e.g. the ones available in the Inertial Measurement Units and wheel odometry sensors. Evaluation of different SLAM algorithms and possible hardware configurations in real environments is time consuming and expensive. For that reason, in this paper we evaluate the performance of different hardware configuration used with Google Cartographer SLAM algorithms in simulation framework proposed in 1. Our use case is an actual robot used for room decontamination. The results show that for our robot the best hardware configuration consists of three LiDARs 2D, IMU and wheel odometry sensors. The proposed simulation-based methodology is a cost-effective alternative to real-world evaluation. It allows easy automation and provides access to precise ground truth. It is especially beneficial in the early stages of product design and to reduce the number of necessary real-life tests and hardware configurations.

Evidence-Based Social Sciences: A New Emerging Field

2018 ◽  
Vol 5 (2) ◽  
pp. 207-211
Author(s):  
Nazila Zarghi ◽  
Soheil Dastmalchian Khorasani
Keyword(s):  
Social Sciences ◽  
Critical Appraisal ◽  
State Of The Art ◽  
Internal Validity ◽  
Evidence Based ◽  
Multiple Sources ◽  
Social Settings ◽  
Research Findings ◽  
Academic Area ◽  
Different Levels

Abstract Evidence based social sciences, is one of the state-of- the-art area in this field. It is making decisions on the basis of conscientious, explicit and judicious use of the best available evidence from multiple sources. It also could be conducive to evidence based social work, i.e a kind of evidence based practice in some extent. In this new emerging field, the research findings help social workers in different levels of social sciences such as policy making, management, academic area, education, and social settings, etc.When using research in real setting, it is necessary to do critical appraisal, not only for trustingon internal validity or rigor methodology of the paper, but also for knowing in what extent research findings could be applied in real setting. Undoubtedly, the latter it is a kind of subjective judgment. As social sciences findings are highly context bound, it is necessary to pay more attention to this area. The present paper tries to introduce firstly evidence based social sciences and its importance and then propose criteria for critical appraisal of research findings for application in society.

scholarly journals G-Tric: generating three-way synthetic datasets with triclustering solutions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
João Lobo ◽  
Rui Henriques ◽  
Sara C. Madeira
Keyword(s):  
State Of The Art ◽  
Synthetic Data ◽  
Ground Truth ◽  
Real Data ◽  
Three Dimensions ◽  
Additional Advantage ◽  
Urban Dynamics ◽  
Data Generator ◽  
Real World Datasets ◽  
Synthetic Datasets

Abstract Background Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations $$\times$$ × features $$\times$$ × contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. Results G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Conclusions Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.

scholarly journals COVID-19 infection map generation and detection from chest X-ray images

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Aysen Degerli ◽  
Mete Ahishali ◽  
Mehmet Yamac ◽  
Serkan Kiranyaz ◽  
Muhammad E. H. Chowdhury ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Clinical Use ◽  
X Ray ◽  
Learning Techniques ◽  
Map Generation ◽  
Severity Grading ◽  
Chest X Ray ◽  
Novel Method ◽  
Aided Diagnosis

AbstractComputer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

scholarly journals A Multi-Branch U-Net for Steel Surface Defect Type and Severity Segmentation

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 870
Author(s):  
Robby Neven ◽  
Toon Goedemé
Keyword(s):  
Process Parameters ◽  
Visual Inspection ◽  
Steel Surface ◽  
Surface Defect ◽  
State Of The Art ◽  
Sheet Steel ◽  
Real Life ◽  
Single Task ◽  
Severity Estimation ◽  
Task Model

Automating sheet steel visual inspection can improve quality and reduce costs during its production. While many manufacturers still rely on manual or traditional inspection methods, deep learning-based approaches have proven their efficiency. In this paper, we go beyond the state-of-the-art in this domain by proposing a multi-task model that performs both pixel-based defect segmentation and severity estimation of the defects in one two-branch network. Additionally, we show how incorporation of the production process parameters improves the model’s performance. After manually constructing a real-life industrial dataset, we first implemented and trained two single-task models performing the defect segmentation and severity estimation tasks separately. Next, we compared this to a multi-task model that simultaneously performs the two tasks at hand. By combining the tasks into one model, both segmentation tasks improved by 2.5% and 3% mIoU, respectively. In the next step, we extended the multi-task model using sensor fusion with process parameters. We demonstrate that the incorporation of the process parameters resulted in a further mIoU increase of 6.8% and 2.9% for the defect segmentation and severity estimation tasks, respectively.

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