scholarly journals Faster R-CNN and Geometric Transformation-Based Detection of Driver’s Eyes Using Multiple Near-Infrared Camera Sensors

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 197 ◽  
Author(s):  
Sung Ho Park ◽  
Hyo Sik Yoon ◽  
Kang Ryoung Park
Keyword(s):  
Near Infrared ◽  
Infrared Camera ◽  
Input Image ◽  
Geometric Transformation ◽  
Data Set ◽  
Gaze Tracking ◽  
Multiple Camera ◽  
Computation Process ◽  
Driver Inattention ◽  
Camera Sensors

Studies are being actively conducted on camera-based driver gaze tracking in a vehicle environment for vehicle interfaces and analyzing forward attention for judging driver inattention. In existing studies on the single-camera-based method, there are frequent situations in which the eye information necessary for gaze tracking cannot be observed well in the camera input image owing to the turning of the driver’s head during driving. To solve this problem, existing studies have used multiple-camera-based methods to obtain images to track the driver’s gaze. However, this method has the drawback of an excessive computation process and processing time, as it involves detecting the eyes and extracting the features of all images obtained from multiple cameras. This makes it difficult to implement it in an actual vehicle environment. To solve these limitations of existing studies, this study proposes a method that uses a shallow convolutional neural network (CNN) for the images of the driver’s face acquired from two cameras to adaptively select camera images more suitable for detecting eye position; faster R-CNN is applied to the selected driver images, and after the driver’s eyes are detected, the eye positions of the camera image of the other side are mapped through a geometric transformation matrix. Experiments were conducted using the self-built Dongguk Dual Camera-based Driver Database (DDCD-DB1) including the images of 26 participants acquired from inside a vehicle and the Columbia Gaze Data Set (CAVE-DB) open database. The results confirmed that the performance of the proposed method is superior to those of the existing methods.

scholarly journals Comparison of Spectroscopic Techniques Combined with Chemometrics for Cocaine Powder Analysis

2020 ◽  
Vol 44 (8) ◽  
pp. 851-860
Author(s):  
Joy Eliaerts ◽  
Natalie Meert ◽  
Pierre Dardenne ◽  
Vincent Baeten ◽  
Juan-Antonio Fernandez Pierna ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Near Infrared ◽  
Evaluation Criteria ◽  
Classification Model ◽  
Support Vector ◽  
Spectroscopic Techniques ◽  
Data Set ◽  
Promising Tool ◽  
Powder Analysis ◽  
Mir Spectra

Abstract Spectroscopic techniques combined with chemometrics are a promising tool for analysis of seized drug powders. In this study, the performance of three spectroscopic techniques [Mid-InfraRed (MIR), Raman and Near-InfraRed (NIR)] was compared. In total, 364 seized powders were analyzed and consisted of 276 cocaine powders (with concentrations ranging from 4 to 99 w%) and 88 powders without cocaine. A classification model (using Support Vector Machines [SVM] discriminant analysis) and a quantification model (using SVM regression) were constructed with each spectral dataset in order to discriminate cocaine powders from other powders and quantify cocaine in powders classified as cocaine positive. The performances of the models were compared with gas chromatography coupled with mass spectrometry (GC–MS) and gas chromatography with flame-ionization detection (GC–FID). Different evaluation criteria were used: number of false negatives (FNs), number of false positives (FPs), accuracy, root mean square error of cross-validation (RMSECV) and determination coefficients (R2). Ten colored powders were excluded from the classification data set due to fluorescence background observed in Raman spectra. For the classification, the best accuracy (99.7%) was obtained with MIR spectra. With Raman and NIR spectra, the accuracy was 99.5% and 98.9%, respectively. For the quantification, the best results were obtained with NIR spectra. The cocaine content was determined with a RMSECV of 3.79% and a R2 of 0.97. The performance of MIR and Raman to predict cocaine concentrations was lower than NIR, with RMSECV of 6.76% and 6.79%, respectively and both with a R2 of 0.90. The three spectroscopic techniques can be applied for both classification and quantification of cocaine, but some differences in performance were detected. The best classification was obtained with MIR spectra. For quantification, however, the RMSECV of MIR and Raman was twice as high in comparison with NIR. Spectroscopic techniques combined with chemometrics can reduce the workload for confirmation analysis (e.g., chromatography based) and therefore save time and resources.

scholarly journals Search for giant planets around seven white dwarfs in the Hyades cluster with the Hubble Space Telescope

2020 ◽  
Vol 500 (3) ◽  
pp. 3920-3925
Author(s):  
Wolfgang Brandner ◽  
Hans Zinnecker ◽  
Taisiya Kopytova
Keyword(s):  
White Dwarfs ◽  
Near Infrared ◽  
Hubble Space Telescope ◽  
Infrared Camera ◽  
Detection Limits ◽  
Giant Planets ◽  
High Angular Resolution ◽  
Mass Detection ◽  
Space Telescope ◽  
Hyades Cluster

ABSTRACT Only a small number of exoplanets have been identified in stellar cluster environments. We initiated a high angular resolution direct imaging search using the Hubble Space Telescope (HST) and its Near-Infrared Camera and Multi-Object Spectrometer (NICMOS) instrument for self-luminous giant planets in orbit around seven white dwarfs in the 625 Myr old nearby (≈45 pc) Hyades cluster. The observations were obtained with Near-Infrared Camera 1 (NIC1) in the F110W and F160W filters, and encompass two HST roll angles to facilitate angular differential imaging. The difference images were searched for companion candidates, and radially averaged contrast curves were computed. Though we achieve the lowest mass detection limits yet for angular separations ≥0.5 arcsec, no planetary mass companion to any of the seven white dwarfs, whose initial main-sequence masses were >2.8 M⊙, was found. Comparison with evolutionary models yields detection limits of ≈5–7 Jupiter masses (MJup) according to one model, and between 9 and ≈12 MJup according to another model, at physical separations corresponding to initial semimajor axis of ≥5–8 au (i.e. before the mass-loss events associated with the red and asymptotic giant branch phase of the host star). The study provides further evidence that initially dense cluster environments, which included O- and B-type stars, might not be highly conducive to the formation of massive circumstellar discs, and their transformation into giant planets (with m ≥ 6 MJup and a ≥6 au). This is in agreement with radial velocity surveys for exoplanets around G- and K-type giants, which did not find any planets around stars more massive than ≈3 M⊙.

scholarly journals Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation

2021 ◽  
Vol 11 (6) ◽  
pp. 701
Author(s):  
Cheng-Hsuan Chen ◽  
Kuo-Kai Shyu ◽  
Cheng-Kai Lu ◽  
Chi-Wen Jao ◽  
Po-Lei Lee
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Kernel Functions ◽  
Olfactory Stimulation ◽  
Support Vector ◽  
Hemodynamic Response Function ◽  
Functional Near Infrared Spectroscopy ◽  
Data Set

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.

scholarly journals PANIC: a near-infrared camera for the Magellan telescopes

2004 ◽  
Author(s):  
Paul Martini ◽  
S. E. Persson ◽  
David C. Murphy ◽  
Christoph Birk ◽  
Stephen A. Shectman ◽  
...  
Keyword(s):  
Near Infrared ◽  
Infrared Camera

Real time injecting device with automated robust vein detection using near infrared camera and live video

2017 ◽  
Author(s):  
Tamim Ahmed ◽  
Khandker Sadia Rahman ◽  
Sk Subrina Shawlin ◽  
Mohammad Hasan ◽  
Arnab Bhattacharjee ◽  
...  
Keyword(s):  
Real Time ◽  
Near Infrared ◽  
Infrared Camera ◽  
Live Video

Applying Artificial Neural Networks. I. Estimating Nicotine in Tobacco from near Infrared Data

1995 ◽  
Vol 3 (3) ◽  
pp. 133-142 ◽  
Author(s):  
M. Hana ◽  
W.F. McClure ◽  
T.B. Whitaker ◽  
M. White ◽  
D.R. Bahler
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Near Infrared ◽  
Back Propagation ◽  
Linear Network ◽  
Data Set ◽  
Input Layer ◽  
Propagation Network ◽  
Better Than

Two artificial neural network models were used to estimate the nicotine in tobacco: (i) a back-propagation network and (ii) a linear network. The back-propagation network consisted of an input layer, an output layer and one hidden layer. The linear network consisted of an input layer and an output layer. Both networks used the generalised delta rule for learning. Performances of both networks were compared to the multiple linear regression method MLR of calibration. The nicotine content in tobacco samples was estimated for two different data sets. Data set A contained 110 near infrared (NIR) spectra each consisting of reflected energy at eight wavelengths. Data set B consisted of 200 NIR spectra with each spectrum having 840 spectral data points. The Fast Fourier transformation was applied to data set B in order to compress each spectrum into 13 Fourier coefficients. For data set A, the linear regression model gave better results followed by the back-propagation network which was followed by the linear network. The true performance of the linear regression model was better than the back-propagation and the linear networks by 14.0% and 18.1%, respectively. For data set B, the back-propagation network gave the best result followed by MLR and the linear network. Both the linear network and MLR models gave almost the same results. The true performance of the back-propagation network model was better than the MLR and linear network by 35.14%.

469 ASSESSMENT OF PERFUSION OF GASTRIC CONDUIT AND PROXIMAL ESOPHAGEAL STUMP BY INDOCYANINE GREEN FLUORESCENCE IMAGING IN PATIENTS UNDERGOING ESOPHAGECTOMY

2021 ◽  
Vol 34 (Supplement_1) ◽  
Author(s):  
Subramanyeshwar Rao Thammineedi
Keyword(s):  
Indocyanine Green ◽  
Fluorescence Imaging ◽  
Visual Inspection ◽  
Near Infrared ◽  
Infrared Camera ◽  
Visual Assessment ◽  
Transhiatal Esophagectomy ◽  
Gastric Conduit ◽  
Indocyanine Green Fluorescence ◽  
Laparoscopic Assisted

Abstract   Post esophagectomy anastomotic leakage and stricture are crucial factors in determining morbidity and mortality. Good vascularity of the gastric conduit is essential to avoid this complications. This prospective study assesses the utility of intraoperative indocyanine green (ICG) fluorescence imaging to determine gastric conduit vascularity in patients undergoing esophagectomy. Methods Thirteen consecutive patients who were undergoing esophagectomy for carcinoma middle, lower third esophagus or gastro-esophageal junction from August 2019 to September 2019, were included. Three patients underwent laparoscopic-assisted transhiatal esophagectomy, ten thoraco-laparoscopic assisted esophagectomy. Reconstruction was done by gastric pull up via posterior mediastinal route. Vascularity of gastric conduit was assessed by the near-infrared camera using ICG. Results On visual assessment of perfusion at the tip of gastric conduit, it was dusky in 11 patients, pink in two. Fuorescence imaging showed inadequate perfusion at the tip of conduit in 12 patients, needing revision. In one patient visual inspection showed adequate perfusion, but ICG disclosed poor vascularity requiring revision of the conduit’s tip. Resection of the devitalized portion of the proximal esophageal stump was needed in 5 patients both by visual and ICG assessment. The median time to appearance of blush from the time of injection of dye was 15 seconds (10 to 23 seconds). Conclusion Visual inspection of the gastric conduit vascularity can underestimate perfusion and hence can compromise resection of the devitalized part. ICG fluorescence imaging is more objective and promising means to ascertain the vascularity of gastric conduit during an esophagectomy. It could complement the visual inspection to decide the site of anastomosis.

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