2,608 research outputs found

    A Multiple-Expert Binarization Framework for Multispectral Images

    Full text link
    In this work, a multiple-expert binarization framework for multispectral images is proposed. The framework is based on a constrained subspace selection limited to the spectral bands combined with state-of-the-art gray-level binarization methods. The framework uses a binarization wrapper to enhance the performance of the gray-level binarization. Nonlinear preprocessing of the individual spectral bands is used to enhance the textual information. An evolutionary optimizer is considered to obtain the optimal and some suboptimal 3-band subspaces from which an ensemble of experts is then formed. The framework is applied to a ground truth multispectral dataset with promising results. In addition, a generalization to the cross-validation approach is developed that not only evaluates generalizability of the framework, it also provides a practical instance of the selected experts that could be then applied to unseen inputs despite the small size of the given ground truth dataset.Comment: 12 pages, 8 figures, 6 tables. Presented at ICDAR'1

    GNSS Vulnerabilities and Existing Solutions:A Review of the Literature

    Get PDF
    This literature review paper focuses on existing vulnerabilities associated with global navigation satellite systems (GNSSs). With respect to the civilian/non encrypted GNSSs, they are employed for proving positioning, navigation and timing (PNT) solutions across a wide range of industries. Some of these include electric power grids, stock exchange systems, cellular communications, agriculture, unmanned aerial systems and intelligent transportation systems. In this survey paper, physical degradations, existing threats and solutions adopted in academia and industry are presented. In regards to GNSS threats, jamming and spoofing attacks as well as detection techniques adopted in the literature are surveyed and summarized. Also discussed are multipath propagation in GNSS and non line-of-sight (NLoS) detection techniques. The review also identifies and discusses open research areas and techniques which can be investigated for the purpose of enhancing the robustness of GNSS

    Template Generation from Postmarks Using Cascaded Unsupervised Learning

    Get PDF
    Information in historical datasets comes in many forms. We are working with a set of World War I era postcards that contain hand written text, some preprinted text, postage stamps and postmark/cancellation stamps. The postmarks are of considerable interest to collectors looking for images of samples they had not previously seen. The postmarks also provide information on the originating location of the card that complements the information in the address block. The postmarks vary considerably with towns and dates, but also styles. The styles can be grouped into categories. A method for automatically extracting templates for each category of these postmark stamps is described. The problem is complicated by the high levels of degradation present in the cards. The approach uses a cascade of unsupervised learning steps separated with image cleaning. This introduces averaging steps, which reduces noise. It also provides a reduction in the number of comparisons between samples. While merges happen at each stage, the number of times merges are needed within each stage is reduced. The templates once extracted can be used to group the postmarks, and will contribute information about the postmark content to better separate the postmark from the paper and other interfering marks to extract further information about the postmarks and postcards

    Modeling Temporal Evidence from External Collections

    Full text link
    Newsworthy events are broadcast through multiple mediums and prompt the crowds to produce comments on social media. In this paper, we propose to leverage on this behavioral dynamics to estimate the most relevant time periods for an event (i.e., query). Recent advances have shown how to improve the estimation of the temporal relevance of such topics. In this approach, we build on two major novelties. First, we mine temporal evidences from hundreds of external sources into topic-based external collections to improve the robustness of the detection of relevant time periods. Second, we propose a formal retrieval model that generalizes the use of the temporal dimension across different aspects of the retrieval process. In particular, we show that temporal evidence of external collections can be used to (i) infer a topic's temporal relevance, (ii) select the query expansion terms, and (iii) re-rank the final results for improved precision. Experiments with TREC Microblog collections show that the proposed time-aware retrieval model makes an effective and extensive use of the temporal dimension to improve search results over the most recent temporal models. Interestingly, we observe a strong correlation between precision and the temporal distribution of retrieved and relevant documents.Comment: To appear in WSDM 201

    DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation

    Full text link
    Image spam emails are often used to evade text-based spam filters that detect spam emails with their frequently used keywords. In this paper, we propose a new image spam email detection tool called DeepCapture using a convolutional neural network (CNN) model. There have been many efforts to detect image spam emails, but there is a significant performance degrade against entirely new and unseen image spam emails due to overfitting during the training phase. To address this challenging issue, we mainly focus on developing a more robust model to address the overfitting problem. Our key idea is to build a CNN-XGBoost framework consisting of eight layers only with a large number of training samples using data augmentation techniques tailored towards the image spam detection task. To show the feasibility of DeepCapture, we evaluate its performance with publicly available datasets consisting of 6,000 spam and 2,313 non-spam image samples. The experimental results show that DeepCapture is capable of achieving an F1-score of 88%, which has a 6% improvement over the best existing spam detection model CNN-SVM with an F1-score of 82%. Moreover, DeepCapture outperformed existing image spam detection solutions against new and unseen image datasets.Comment: 15 pages, single column. ACISP 2020: Australasian Conference on Information Security and Privac

    Local Image Patterns for Counterfeit Coin Detection and Automatic Coin Grading

    Get PDF
    Abstract Local Image Patterns for Counterfeit Coin Detection and Automatic Coin Grading Coins are an essential part of our life, and we still use them for everyday transactions. We have always faced the issue of the counterfeiting of the coins, but it has become worse with time due to the innovation in the technology of counterfeiting, making it more difficult for detection. Through this thesis, we propose a counterfeit coin detection method that is robust and applicable to all types of coins, whether they have letters on them or just images or both of these characteristics. We use two different types of feature extraction methods. The first one is SIFT (Scale Invariant Feature transform) features, and the second one is RFR (Rotation and Flipping invariant Regional Binary Patterns) features to make our system complete in all aspects and very generic at the same time. The feature extraction methods used here are scale, rotation, illumination, and flipping invariant. We concatenate both our feature sets and use them to train our classifiers. Our feature sets highly complement each other in a way that SIFT provides us with most discriminative features that are scale and rotation invariant but do not consider the spatial value when we cluster them, and here our second set of features comes into play as it considers the spatial structure of each coin image. We train SVM classifiers with two different sets of features from each image. The method has an accuracy of 99.61% with both high and low-resolution images. We also took pictures of the coins at 90˚ and 45˚ angles using the mobile phone camera, to check the robustness of our proposed method, and we achieved promising results even with these low-resolution pictures. Also, we work on the problem of Coin Grading, which is another issue in the field of numismatic studies. Our algorithm proposed above is customized according to the coin grading problem and calculates the coin wear and assigns a grade to it. We can use this grade to remove low-quality coins from the system, which are otherwise sold to coin collectors online for a considerable price. Coin grading is currently done by coin experts manually and is a time consuming and expensive process. We use digital images and apply computer vision and machine learning algorithms to calculate the wear on the coin and then assign it a grade based on its quality level. Our method calculates the amount of wear on coins and assign them a label and achieve an accuracy of 98.5%

    The star formation history of mass-selected galaxies in the COSMOS field

    Get PDF
    We explore the evolution of the specific star formation rate (SSFR) for 3.6um-selected galaxies of different M_* in the COSMOS field. The average SFR for sub-sets of these galaxies is estimated with stacked 1.4GHz radio continuum emission. We separately consider the total sample and a subset of galaxies (SF) that shows evidence for substantive recent star formation in the rest-frame optical SED. At 0.2<z<3 both populations show a strong and M_*-independent decrease in their SSFR towards z=0.2, best described by a power- law (1+z)^n, where n~4.3 for all galaxies and n~3.5 for SF sources. The decrease appears to have started at z>2, at least above 4x10^10M_Sun where our conclusions are most robust. We find a tight correlation with power-law dependence, SSFR (M_*)^beta, between SSFR and M_* at all z. It tends to flatten below ~10^10M_Sun if quiescent galaxies are included; if they are excluded a shallow index beta_SFG -0.4 fits the correlation. On average, higher M_* objects always have lower SSFRs, also among SF galaxies. At z>1.5 there is tentative evidence for an upper SSFR-limit that an average galaxy cannot exceed. It is suggested by a flattening of the SSFR-M_* relation (also for SF sources), but affects massive (>10^10M_Sun) galaxies only at the highest z. Below z=1.5 there thus is no direct evidence that galaxies of higher M_* experience a more rapid waning of their SSFR than lower M_* SF systems. In this sense, the data rule out any strong 'downsizing'. We combine our results with recent measurements of the galaxy (stellar) mass function in order to determine the characteristic mass of a SF galaxy (M_*=10^(10.6\pm0.4)M_Sun). In this sense, too, there is no 'downsizing'. Our analysis constitutes the most extensive SFR density determination with a single technique to z=3. Recent Herschel results are consistent with our results, but rely on far smaller samples.Comment: 37 pages, 14 figures, 7 tables; accepted for publication in the Astrophysical Journal; High resolution versions of all figures available at www.mpia-hd.mpg.de/homes/karim/research.htm

    Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1

    Get PDF
    This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines

    Review on the analysis of questioned documents

    Get PDF
    During the last years (2000-2014), many publications concerning the forensic analysis of questioned documents have been published, and new techniques and methodologies arenowadays employed to overcome forensic caseworks. This article reviews a comprehensive collection of the works focused on this issue, dating studies, the analysis of inks from pens andprinters, the analysis of paper, the analysis of other samples related to questioned documents and studies on intersecting lines. These sections highlight the most relevant analytical studies by a wide range of analytical techniques. Separation and spectrometric techniques are critically discussed and compared, emphasizing the advantages and disadvantages of each one. Finally, concluding remarks on the research published are included
    corecore