Copy-Move Forgery Detection by Matching Triangles of Keypoints Ardizzone, Edoardo; Bruno, Alessandro; Mazzola, Giuseppe
IEEE transactions on information forensics and security,
2015-Oct., 2015-10-00, 20151001, Letnik:
10, Številka:
10
Journal Article
Recenzirano
Copy-move forgery is one of the most common types of tampering for digital images. Detection methods generally use block-matching approaches, which first divide the image into overlapping blocks and ...then extract and compare features to find similar ones, or point-based approaches, in which relevant keypoints are extracted and matched to each other to find similar areas. In this paper, we present a very novel hybrid approach, which compares triangles rather than blocks, or single points. Interest points are extracted from the image, and objects are modeled as a set of connected triangles built onto these points. Triangles are matched according to their shapes (inner angles), their content (color information), and the local feature vectors extracted onto the vertices of the triangles. Our methods are designed to be robust to geometric transformations. Results are compared with a state-of-the-art block matching method and a point-based method. Furthermore, our data set is available for use by academic researchers.
The first case of Coronavirus Disease 2019 in Italy was detected on February the 20th in Lombardy region. Since that date, Lombardy has been the most affected Italian region by the epidemic, and its ...healthcare system underwent a severe overload during the outbreak. From a public health point of view, therefore, it is fundamental to provide healthcare services with tools that can reveal possible new health system stress periods with a certain time anticipation, which is the main aim of the present study. Moreover, the sequence of law decrees to face the epidemic and the large amount of news generated in the population feelings of anxiety and suspicion. Considering this whole complex context, it is easily understandable how people "overcrowded" social media with messages dealing with the pandemic, and emergency numbers were overwhelmed by the calls. Thus, in order to find potential predictors of possible new health system overloads, we analysed data both from Twitter and emergency services comparing them to the daily infected time series at a regional level. Particularly, we performed a wavelet analysis in the time-frequency plane, to finely discriminate over time the anticipation capability of the considered potential predictors. In addition, a cross-correlation analysis has been performed to find a synthetic indicator of the time delay between the predictor and the infected time series. Our results show that Twitter data are more related to social and political dynamics, while the emergency calls trends can be further evaluated as a powerful tool to potentially forecast new stress periods. Since we analysed aggregated regional data, and taking into account also the huge geographical heterogeneity of the epidemic spread, a future perspective would be to conduct the same analysis on a more local basis.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Castiglione D'Adda is one of the municipalities more precociously and severely affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) epidemic in Lombardy. With our study we ...aimed to understand the diffusion of the infection by mass serological screening. We searched for SARS-CoV-2 IgGs in the entire population on a voluntary basis using lateral flow immunochromatographic tests (RICT) on capillary blood (rapid tests). We then performed chemioluminescent serological assays (CLIA) and naso-pharyngeal swabs (NPS) in a randomized representative sample and in each subject with a positive rapid test. Factors associated with RICT IgG positivity were assessed by uni- and multivariate logistic regression models. Out of the 4143 participants, 918 (22·2%) showed RICT IgG positivity. In multivariable analysis, IgG positivity increases with age, with a significant non-linear effect (p = 0·0404). We found 22 positive NPSs out of the 1330 performed. Albeit relevant, the IgG prevalence is lower than expected and suggests that a large part of the population remains susceptible to the infection. The observed differences in prevalence might reflect a different infection susceptibility by age group. A limited persistence of active infections could be found after several weeks after the epidemic peak in the area.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging ...modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper’s main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.
Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods' performance heavily depends ...on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.
ALICE is a general purpose experiment designed to investigate nucleus-nucleus collisions at the CERN Large Hadron Collider (LHC). Located 52 meters underground, with 28 meters of overburden rock, it ...has also been used to detect the muonic component of the extensive air showers produced by cosmic-ray interactions in the upper atmosphere. A program of cosmic-ray data taking, with specific triggers for atmospheric muons, was started in 2010 in periods when there is no beam circulating in the LHC. Several million events have been recorded to date. The large size and excellent tracking capability of the ALICE Time Projection Chamber are exploited to detect and reconstruct these muons. In this paper the analysis of the multiplicity distribution of the atmospheric muons detected by ALICE between 2010 and 2013 is presented, along with the comparison with Monte Carlo simulations. Special emphasis is given to the study of high multiplicity events containing more than 100 reconstructed muons. The comprehension of the frequency of these events was an unsolved problem since the pioneering studies performed by ALEPH and DELPHI experiments at LEP. In our work the ALICE measurements show that such high multiplicity events demand primary cosmic rays with energy above 1016 eV. Their frequency can be successfully described by assuming a heavy mass composition of primary cosmic rays above this energy and using the most recent interaction models to describe the development of the air shower resulting from the primary interaction.
Relativistic electron precipitation (REP) refers to the release of high‐energy electrons initially trapped in the outer radiation belt, which then precipitate into Earth's upper atmosphere, ...contributing significantly to the rapid depletion of radiation belt electron flux. This study presents a statistical analysis of REP observations collected by the Calorimetric Electron Telescope (CALET) experiment aboard the International Space Station from 2015 to the present day. Specifically, the analysis utilizes count rates acquired from the two top scintillators constituting the top charge detector, each sensitive to electrons with energies above 1.5 and 3.4 MeV, respectively. Analysis of CALET data reveals a previously unreported semi‐annual variation in the occurrence of REP events. REP periodicities resemble those observed for trapped electron fluxes in the outer belt. Furthermore, their amplitude follows the overall trend of solar wind high‐speed streams and the solar activity.
Plain Language Summary
Relativistic electron precipitation (REP) refers to the release, toward the upper atmosphere, of high energy electrons initially trapped in a torus shaped region around Earth known as the outer Van Allen radiation belt. REP is relevant as it contributes to the fast depletion of the electrons from this region. This study presents a statistical analysis of the REP observations made by the Calorimetric Electron Telescope (CALET) experiment on board the International Space Station (2015–present). Data from CALET experiment reveals a previously unreported 6‐month periodicity similar to those observed for high energy electrons in the outer belt.
Key Points
Semi‐annual variation of relativistic electron precipitation (REP) have been observed for the first time
Reported periodicities have been compared with those characterizing the outer belt electron fluxes
The temporal variation in the REP and the trapped fluxes were found to be in strong correlation
Abstract
With the introduction of superconducting circuits into the field of quantum optics, many experimental demonstrations of the quantum physics of an artificial atom coupled to a single-mode ...light field have been realized. Engineering such quantum systems offers the opportunity to explore extreme regimes of light-matter interaction that are inaccessible with natural systems. For instance the coupling strength
g
can be increased until it is comparable with the atomic or mode frequency
ω
a
,
m
and the atom can be coupled to multiple modes which has always challenged our understanding of light-matter interaction. Here, we experimentally realize a transmon qubit in the ultra-strong coupling regime, reaching coupling ratios of
g
/
ω
m
= 0.19 and we measure multi-mode interactions through a hybridization of the qubit up to the fifth mode of the resonator. This is enabled by a qubit with 88% of its capacitance formed by a vacuum-gap capacitance with the center conductor of a coplanar waveguide resonator. In addition to potential applications in quantum information technologies due to its small size, this architecture offers the potential to further explore the regime of multi-mode ultra-strong coupling.
This review aims to take a journey into the transformative impact of artificial intelligence (AI) on positron emission tomography (PET) imaging. To this scope, a broad overview of AI applications in ...the field of nuclear medicine and a thorough exploration of deep learning (DL) implementations in cancer diagnosis and therapy through PET imaging will be presented. We firstly describe the behind-the-scenes use of AI for image generation, including acquisition (event positioning, noise reduction though time-of-flight estimation and scatter correction), reconstruction (data-driven and model-driven approaches), restoration (supervised and unsupervised methods), and motion correction. Thereafter, we outline the integration of AI into clinical practice through the applications to segmentation, detection and classification, quantification, treatment planning, dosimetry, and radiomics/radiogenomics combined to tumour biological characteristics. Thus, this review seeks to showcase the overarching transformation of the field, ultimately leading to tangible improvements in patient treatment and response assessment. Finally, limitations and ethical considerations of the AI application to PET imaging and future directions of multimodal data mining in this discipline will be briefly discussed, including pressing challenges to the adoption of AI in molecular imaging such as the access to and interoperability of huge amount of data as well as the “black-box” problem, contributing to the ongoing dialogue on the transformative potential of AI in nuclear medicine.
Relevance statement
AI is rapidly revolutionising the world of medicine, including the fields of radiology and nuclear medicine. In the near future, AI will be used to support healthcare professionals. These advances will lead to improvements in diagnosis, in the assessment of response to treatment, in clinical decision making and in patient management.
Key points
• Applying AI has the potential to enhance the entire PET imaging pipeline.
• AI may support several clinical tasks in both PET diagnosis and prognosis.
• Interpreting the relationships between imaging and multiomics data will heavily rely on AI.
Graphical Abstract
Development of Global Quality Index of Unpaved Roads da Silva, Wallace Orlandini Prado; Farias, Bruno Alessandro; Monteiro, Igo Brasil ...
Journal of construction engineering and management,
01/2024, Letnik:
150, Številka:
1
Journal Article
Recenzirano
Unpaved roads often are in poor condition, especially in developing countries, due to limited resources, resulting in discomfort, accident risk, vehicle operating costs (VOCs), freight transport ...damage, and difficulties accessing essential services by rural populations. Previous studies proposed methods that do not enable a complete and cost-effective unpaved road evaluation in the context of an integrated unpaved road management system (URMS). Developing global condition indexes is crucial in establishing a hierarchical classification of the whole road network, rationalizing resource allocation, and facilitating planning maintenance and rehabilitation candidate projects within a medium to long timeframe. The Global Quality Index of Unpaved Roads (GQIUR) development in this work provides data for management at the network and project levels, allowing the evaluation of riding quality and both distress at specific road sections. The GQIUR combines the Ride Quality Index of Unpaved Roads (RQIUR), proposed in this study, with the Unsurfaced Road Condition Index (URCI). By capturing data from cameras, accelerometers, and Global Positioning System (GPS) sensors on smartphones affixed to a vehicle’s windshield, it is possible to determine the RQIUR by evaluating ride quality while traveling. Recording surface distresses during walking surveys allows URCI calculation. The URCI and RQIUR classification was established based on the existing literature and the practicality of URMS. The GQIUR incorporates these attributes in a balanced manner, considering the comparable importance of the URCI and RQIUR for managers and users. The evaluation covered more than 10 km of unpaved roads. Asphalt and cobblestone pavement samples were compared with unpaved road data. GIS application to the GQIUR shows the general classification of unpaved roads. Unpaved roads present structural and functional distresses, and gravel roads cause excessive vibrations due to roughness and loose aggregates. The method enables priority section identification in a practical, objective, and cost-effective manner.
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