scholarly journals Robust Finger-vein ROI Localization Based on the 3σ Criterion Dynamic Threshold Strategy

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3997 ◽  
Author(s):  
Qiong Yao ◽  
Dan Song ◽  
Xiang Xu
Keyword(s):  
Region Of Interest ◽  
Dynamic Threshold ◽  
Identification System ◽  
Horizontal Line ◽  
Threshold Strategy ◽  
Edge Information ◽  
Finger Vein ◽  
Localization Performance ◽  
The Impact ◽  
3Σ Criterion

Region of interest (ROI) localization is one of the key preprocessing technologies for a finger-vein identification system, so an effective ROI definition can improve the matching accuracy. However, due to the impact of uneven illumination, equipment noise, as well as the distortion of finger position, etc., these make accurate ROI localization a very difficult task. To address these issues, in this paper, we propose a robust finger-vein ROI localization method, which is based on the 3 σ criterion dynamic threshold strategy. The proposed method includes three main steps: First, the Kirsch edge detector is introduced to detect the horizontal-like edges in the acquired finger-vein image. Then, the obtained edge gradient image is divided into four parts: upper-left, upper-right, lower-left, and lower-right. For each part of the image, the three-level dynamic threshold, which is based on the 3 σ criterion of the normal distribution, is imposed to obtain more distinct and complete edge information. Finally, through labeling the longest connected component at the same horizontal line, two reliable finger boundaries, which represent the upper and lower boundaries, respectively, are defined, and the ROI is localized in the region between these two boundaries. Extensive experiments are carried out on four different finger-vein image datasets, including three publicly available datasets and one of our newly developed finger-vein datasets with 37,080 finger-vein samples and 1030 individuals. The experimental results indicate that our proposed method has very competitive ROI localization performance, as well as satisfactory matching results on different datasets.

scholarly journals Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images

2020 ◽  
Vol 6 ◽  
pp. e248 ◽  
Author(s):  
El mehdi Cherrat ◽  
Rachid Alaoui ◽  
Hassane Bouzahir
Keyword(s):  
High Performance ◽  
Personal Data ◽  
Region Of Interest ◽  
Identification System ◽  
Exposure Fusion ◽  
Finger Vein ◽  
Face Images ◽  
Image Contrast Enhancement ◽  
Personal Recognition ◽  
Efficient Matching

In recent years, the need for security of personal data is becoming progressively important. In this regard, the identification system based on fusion of multibiometric is most recommended for significantly improving and achieving the high performance accuracy. The main purpose of this paper is to propose a hybrid system of combining the effect of tree efficient models: Convolutional neural network (CNN), Softmax and Random forest (RF) classifier based on multi-biometric fingerprint, finger-vein and face identification system. In conventional fingerprint system, image pre-processed is applied to separate the foreground and background region based on K-means and DBSCAN algorithm. Furthermore, the features are extracted using CNNs and dropout approach, after that, the Softmax performs as a recognizer. In conventional fingervein system, the region of interest image contrast enhancement using exposure fusion framework is input into the CNNs model. Moreover, the RF classifier is proposed for classification. In conventional face system, the CNNs architecture and Softmax are required to generate face feature vectors and classify personal recognition. The score provided by these systems is combined for improving Human identification. The proposed algorithm is evaluated on publicly available SDUMLA-HMT real multimodal biometric database using a GPU based implementation. Experimental results on the datasets has shown significant capability for identification biometric system. The proposed work can offer an accurate and efficient matching compared with other system based on unimodal, bimodal, multimodal characteristics.

scholarly journals Improving Near Miss Detection in Maritime Traffic in the Northern Baltic Sea from AIS Data

2021 ◽  
Vol 9 (2) ◽  
pp. 180
Author(s):  
Lei Du ◽  
Osiris A. Valdez Banda ◽  
Floris Goerlandt ◽  
Pentti Kujala ◽  
Weibin Zhang
Keyword(s):  
Baltic Sea ◽  
Perceived Risk ◽  
Maritime Transportation ◽  
Near Miss ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Ship Collision ◽  
The Impact ◽  
Ship Behavior

Ship collision is the most common type of accident in the Northern Baltic Sea, posing a risk to the safety of maritime transportation. Near miss detection from automatic identification system (AIS) data provides insight into maritime transportation safety. Collision risk always triggers a ship to maneuver for safe passing. Some frenetic rudder actions occur at the last moment before ship collision. However, the relationship between ship behavior and collision risk is not fully clarified. Therefore, this work proposes a novel method to improve near miss detection by analyzing ship behavior characteristic during the encounter process. The impact from the ship attributes (including ship size, type, and maneuverability), perceived risk of a navigator, traffic complexity, and traffic rule are considered to obtain insights into the ship behavior. The risk severity of the detected near miss is further quantified into four levels. This proposed method is then applied to traffic data from the Northern Baltic Sea. The promising results of near miss detection and the model validity test suggest that this work contributes to the development of preventive measures in maritime management to enhance to navigational safety, such as setting a precautionary area in the hotspot areas. Several advantages and limitations of the presented method for near miss detection are discussed.

Research on Biological Materials by Applied Technology with an Algorithm for Identifying and Counting Cells in Microscopic Particle Image

2014 ◽  
Vol 540 ◽  
pp. 352-355
Author(s):  
Sui Yuan Zhang ◽  
Rui Wang ◽  
Xian Qiao Chen ◽  
Ze Wu Jiang ◽  
Xiang Cai
Keyword(s):  
Particle Image ◽  
Dynamic Threshold ◽  
Biological Cells ◽  
Edge Detecting ◽  
Applied Technology ◽  
Channel Information ◽  
High Level ◽  
The Impact ◽  
Fundamental Units ◽  
Rule Out

Cells are fundamental units of life, and the key point in the field of biomaterial. Biological cells are always with high density, small nucleus and much impurities. Based on the technology of image processing, we propose a new method to count cells on the image of microscopic cells with high level of recognition. To precisely count the number, our method includes edge detecting and marking, efficient usage of three channel information of enhanced nucleus, binaryzation of dynamic threshold in separated areas and finally denoising. The experiment shows that the method is precise and quickly-reacted, moreover it can effectively rule out the impact of impurities. With little adjustment, it can apply to some other fields, not only decrease the labor involved, but the budget as well.

The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

2021 ◽  
Author(s):  
Isolde Glissenaar ◽  
Jack Landy ◽  
Alek Petty ◽  
Nathan Kurtz ◽  
Julienne Stroeve
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Satellite Altimetry ◽  
Region Of Interest ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Baffin Bay ◽  
The Impact

<p>The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.</p><p>We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.</p><p>The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.</p>

scholarly journals Influence of Atmospheric Rivers on Mountain Snowpack in the Western United States

2018 ◽  
Vol 31 (24) ◽  
pp. 9921-9940 ◽  
Author(s):  
N. Goldenson ◽  
L. R. Leung ◽  
C. M. Bitz ◽  
E. Blanchard-Wrigglesworth
Keyword(s):  
United States ◽  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
Region Of Interest ◽  
Internal Variability ◽  
Western United States ◽  
Historical Period ◽  
Atmospheric Rivers ◽  
Atmospheric River ◽  
The Impact

In the coastal mountains of western North America, most extreme precipitation is associated with atmospheric rivers (ARs), narrow bands of moisture originating in the tropics. Here we quantify how interannual variability in atmospheric rivers influences snowpack in the western United States in observations and a model. We simulate the historical climate with the Model for Prediction Across Scales (MPAS) with physics from the Community Atmosphere Model, version 5 [CAM5 (MPAS-CAM5)], using prescribed sea surface temperatures. In the global variable-resolution domain, regional refinement (at ~30 km) is applied to our region of interest and upwind over the northeast Pacific. To better characterize internal variability, we conduct simulations with three ensemble members over 30 years of the historical period. In the Cascade Range, with some exceptions, winters with more atmospheric river days are associated with less snowpack. In California’s Sierra Nevada, winters with more ARs are associated with greater snowpack. The slope of the linear regression of observed snow water equivalent (SWE) on reanalysis-based AR count has the same sign as that arrived at using the model, but is statistically significant in observations only for California. In spring, internal variance plays an important role in determining whether atmospheric river days appear to be associated with greater or less snowpack. The cumulative (winter through spring) number of atmospheric river days, on the other hand, has a relationship with spring snowpack, which is consistent across ensemble members. Thus, the impact of atmospheric rivers on winter snowpack has a greater influence on spring snowpack than spring atmospheric rivers in the model for both regions and in California consistently in observations.

scholarly journals NO<sub>2</sub> seasonal evolution in the North Subtropical free troposphere

2015 ◽  
Vol 15 (10) ◽  
pp. 14473-14504
Author(s):  
M. Gil-Ojeda ◽  
M. Navarro-Comas ◽  
L. Gómez-Martín ◽  
J. A. Adame ◽  
A. Saiz-Lopez ◽  
...  
Keyword(s):  
Optical Absorption Spectroscopy ◽  
High Mountain ◽  
Horizontal Line ◽  
Free Troposphere ◽  
Seasonal Evolution ◽  
The North ◽  
Measuring Period ◽  
The Impact ◽  
Pollution Events

Abstract. Three years of Multi-Axis Differential Optical Absorption Spectroscopy (MAXDOAS) measurements (2011–2013) have been used for estimating the NO2 mixing ratio along a horizontal line of sight from the high mountain Subtropical observatory of Izaña, at 2370 m a.s.l. (NDACC station, 28.3° N, 16.5° W). The method is based on horizontal path calculation from the O2–O2 collisional complex at the 477 nm absorption band which is measured simultaneously to the NO2, and is applicable under low aerosols loading conditions. The MAXDOAS technique, applied in horizontal mode in the free troposphere, minimizes the impact of the NO2 contamination resulting from the arrival of MBL airmasses from thermally forced upwelling breeze during central hours of the day. Comparisons with in-situ observations show that during most of measuring period the MAXDOAS is insensitive or very little sensitive to the upwelling breeze. Exceptions are found during pollution events under southern wind conditions. On these occasions, evidence of fast efficient and irreversible transport from the surface to the free troposphere is found. Background NO2 vmr, representative of the remote free troposphere, are in the range of 20–45 pptv. The observed seasonal evolution shows an annual wave where the peak is in phase with the solar radiation. Model simulations with the chemistry-climate CAM-Chem model are in good agreement with the NO2 measurements, and are used to further investigate the possible drivers of the NO2 seasonality observed at Izaña.

scholarly journals Total Electron Content (TEC) estimation over Pakistan from local GPS network using spherical harmonics

2021 ◽  
Vol 64 (1) ◽  
Author(s):  
Maria Mehmood ◽  
Sajid Saleem ◽  
Renato Filjar ◽  
Najam Naqvi ◽  
Arslan Ahmed
Keyword(s):  
Spherical Harmonics ◽  
Orbit Determination ◽  
Region Of Interest ◽  
Local Model ◽  
Small Scale ◽  
Electron Content ◽  
Total Electron ◽  
Gps Network ◽  
Spherical Harmonics Expansion ◽  
The Impact

Many organizations allow GNSS users to access Global Ionosphere Maps (GIMS). However, the TEC estimates derived from GIMs are of insufficient quality to describe small-scale TEC variations over Pakistan. In this paper, the first local TEC map over Pakistan for the year 2019, derived from a regional GPS network, is presented. Spherical harmonics expansion is employed to estimate TEC with the spatial resolution of latitude 0.2° x longitude 0.2° and temporal resolution of 5 minutes. The impact of changing the degree/order of harmonics is assessed and it is determined that harmonic expansion up to 6 degrees is sufficient for estimating accurate TEC map for the region of interest. We have demonstrated that the TEC maps of Pakistan generated by local model conform better to the GIM by Center of Orbit Determination (CODE) (RMS = 5.83) as compared to International Reference Ionosphere (IRI-2016) (RMS = 7.18). We found that the TEC estimated by the local model shows a better correlation to measured TEC; CODE-GIM overestimated TEC, while IRI-2016 underestimates it. Moreover, it was observed that TEC peaks during noon (1100-0100 LT) and Equinox (April). The residuals of local TEC estimates with respect to TEC obtained from CODE- GIM indicate the inaccuracy of CODE-GIM over the region of Pakistan: highest deviation of TEC from local model with respect to CODE –GIM was observed in April (RMS = 8.73) and minimum in October (RMS = 2.78). We have also analyzed the performance of our maps in geomagnetically disturbed days. The research presented in this paper will contribute towards the ionosphere study over Pakistan, where limited research is available currently.

Biometric Authentication Using Finger-Vein Patterns with Deep-Learning and Discriminant Correlation Analysis

2021 ◽  
pp. 2250013
Author(s):  
Aldjia Boucetta ◽  
Leila Boussaad
Keyword(s):  
Correlation Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
Identification System ◽  
Deep Convolutional Neural Networks ◽  
Finger Vein ◽  
Deep Cnn ◽  
Identification Stage ◽  
Human Finger ◽  
The Right

Finger-vein identification, a biometric technology that uses vein patterns in the human finger to identify people. In recent years, it has received increasing attention due to its tremendous advantages compared to fingerprint characteristics. Moreover, Deep-Convolutional Neural Networks (Deep-CNN) appeared to be highly successful for feature extraction in the finger-vein area, and most of the proposed works focus on new Convolutional Neural Network (CNN) models, which require huge databases for training, a solution that may be more practicable in real world applications, is to reuse pretrained Deep-CNN models. In this paper, a finger-vein identification system is proposed, which uses Squeezenet pretrained Deep-CNN model as feature extractor from the left and the right finger vein patterns. Then, combines this Deep-based features by using a feature-level Discriminant Correlation Analysis (DCA) to reduce feature dimensions and to give the most relevant features. Finally, these composite feature vectors are used as input data for a Support Vector Machine (SVM) classifier, in an identification stage. This method is tested on two widely available finger vein databases, namely SDUMLA-HMT and FV-USM. Experimental results show that the proposed finger vein identification system achieves significant high mean accuracy rates.

Implementation Success Model in Government Agencies

2010 ◽  
Vol 2 (2) ◽  
pp. 19-32 ◽  
Author(s):  
Yair Levy ◽  
Theon L. Danet
Keyword(s):  
Self Efficacy ◽  
High Reliability ◽  
User Involvement ◽  
Structural Equations ◽  
Partial Least Square ◽  
Identification System ◽  
Government Agencies ◽  
Implementation Success ◽  
The Impact ◽  
Computer Self Efficacy

A recent presidential directive mandated that all U.S. government agencies establish a centralized identification system. This study investigated the impact of users’ involvement, resistance, and computer self-efficacy on the implementation success of a centralized identification system. Information System (IS) usage was the construct employed to measure IS implementation success. A survey instrument was developed based on existing measures from key IS literature. The results of this study indicated a strong reliability for the measures of all constructs (user involvement, computer self-efficacy, user’s resistance, and IS usage). Factor analysis was conducted using Principal Component Analysis (PCA) with Varimax rotation. Results of the PCA indicate that items of the constructs measured had high validity, while Cronbach’s Alpha for each factor demonstrates high reliability for all constructs measured. Additionally, results of a structural equations modeling analysis using Partial Least Square (PLS) indicate that computer self-efficacy and user involvement had positive significant impact on the implementation success. However, the results also demonstrated that user’s resistance had no significant impact on IS usage, while end user involvement had a strong negative impact on user’s resistance.

Performance Bounds for VDES R-mode

2019 ◽  
Vol 73 (1) ◽  
pp. 92-114 ◽  
Author(s):  
Jan Šafář ◽  
Alan Grant ◽  
Paul Williams ◽  
Nick Ward
Keyword(s):  
Data Exchange ◽  
Estimation Error ◽  
Additive White Gaussian Noise ◽  
Automatic Identification ◽  
Identification System ◽  
Performance Bounds ◽  
Radio Communication ◽  
Very High Frequency ◽  
Radio Communication System ◽  
The Impact

The Very High Frequency (VHF) Data Exchange System (VDES) is a new radio communication system being developed by the international maritime community, with the principal objectives to safeguard existing Automatic Identification System (AIS) core functions and enhance maritime communication applications, based on robust, efficient and secure data transmission with wider bandwidth than the AIS. VDES is also being considered as a potential component of the R-mode concept, where the same signals used for communication are also used for ranging, thus mitigating the impact of disruptions to satellite positioning services. This paper establishes statistical performance bounds on the ranging precision of VDES R-mode, assuming an additive white Gaussian noise propagation channel. Modified Cramér-Rao bounds on the pseudorange estimation error are provided for all waveforms currently proposed for use in terrestrial VDES communications. These are then used to estimate the maximum usable ranges for AIS/VDES R-mode stations. The results show that, under the assumed channel conditions, all of the new VDES waveforms provide better ranging performance than the AIS waveform, with the best performance being achieved using the 100 kHz bandwidth terrestrial VDE waveforms.

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