COVID-19 risk reduce based YOLOv4-P6-FaceMask detector and DeepSORT tracker.

Wearing masks in public areas is one of the effective protection methods for people. Although it is essential to wear the facemask correctly, there are few research studies about facemask detection and tracking based on image processing. In this work, we propose a new high performance two stage facemask detector and tracker with a monocular camera and a deep learning based framework for automating the task of facemask detection and tracking using video sequences. Furthermore, we propose a novel facemask detection dataset consisting of 18,000 images with more than 30,000 tight bounding boxes and annotations for three different class labels namely respectively: face masked/incorrectly masked/no masked. We based on Scaled-You Only Look Once (Scaled-YOLOv4) object detection model to train the YOLOv4-P6-FaceMask detector and Simple Online and Real-time Tracking with a deep association metric (DeepSORT) approach to tracking faces. We suggest using DeepSORT to track faces by ID assignment to save faces only once and create a database of no masked faces. YOLOv4-P6-FaceMask is a model with high accuracy that achieves 93% mean average precision, 92% mean average recall and the real-time speed of 35 fps on single GPU Tesla-T4 graphic card on our proposed dataset. To demonstrate the performance of the proposed model, we compare the detection and tracking results with other popular state-of-the-art models of facemask detection and tracking.

On the Implementation of Simultaneous Multi-Frequency Excitations and Measurements for Electrical Impedance Tomography.

The investigation of quickly-evolving flow patterns in high-pressure and high-temperature flow rigs requires the use of a high-speed and non-intrusive imaging technique. Electrical Impedance Tomography (EIT) allows reconstructing the admittivity distribution characterizing a flow from the knowledge of currents and voltages on its periphery. The need for images at high frame rates leads to the strategy of simultaneous multi-frequency voltage excitations and simultaneous current measurements, which are discriminated using fast Fourier transforms. The present study introduces the theory for a 16-electrode simultaneous EIT system, which is then built based on a field programmable gate array data acquisition system. An analysis of the propagation of uncertainties through the measurement process is investigated, and experimental results with fifteen simultaneous signals are presented. It is shown that the signals are successfully retrieved experimentally at a rate of 1953 frames per second. The associated signal-to-noise ratio varies from 59.6-69.1 dB, depending on the generated frequency. These preliminary results confirm the relevance and the feasibility of simultaneous multi-frequency excitations and measurements in EIT as a means to significantly increase the imaging rate.

Histogram-Based Features Selection and Volume of Interest Ranking for Brain PET Image Classification.

Positron emission tomography (PET) is a molecular medical imaging modality which is commonly used for neurodegenerative diseases diagnosis. Computer-aided diagnosis, based on medical image analysis, could help quantitative evaluation of brain diseases such as Alzheimer's disease (AD). A novel method of ranking the effectiveness of brain volume of interest (VOI) to separate healthy control from AD brains PET images is presented in this paper. Brain images are first mapped into anatomical VOIs using an atlas. Histogram-based features are then extracted and used to select and rank VOIs according to the area under curve (AUC) parameter, which produces a hierarchy of the ability of VOIs to separate between groups of subjects. The top-ranked VOIs are then input into a support vector machine classifier. The developed method is evaluated on a local database image and compared to the known selection feature methods. Results show that using AUC outperforms classification results in the case of a two group separation.

Improving the performance of underwater wireless optical communication links by channel coding.

We investigate the efficacy of error correcting codes in improving the performance of underwater wireless optical communication systems. For this purpose, the effectiveness of several coding schemes, i.e., the classical Reed-Solomon and a recent family of low-density parity check codes, is studied in the physical (PHY) and the upper layers assuming negligible water turbulence. The presented numerical results testify to the interest of using efficient codes both at the PHY and upper protocol layers, although we are concerned by a non-fading channel. Furthermore, we discuss the choice of coding schemes and the appropriate degree of data protection in the PHY and upper layers.

Investigation of solar noise impact on the performance of underwater wireless optical communication links.

We investigate the effect of environmental noise, caused by solar radiations under water, on the performance of underwater wireless optical communication (UWOC) systems. Presenting an analytical and generic model for this noise, we examine its impact on the link performance in terms of the bit error rate (BER). This study is conducted for different photo-detector types in the aim of highlighting practical limitations of establishing UWOC links in the presence of subsea solar noise. We show how the solar noise can impact the performance of UWOC links for relatively low operation depths. The results we present provide valuable insight for the design of UWOC links, which are likely to be established at relatively low depths. They can be exploited not only for the purpose of practical UWOC system deployment but also for in-pool experimental set-ups, since they elucidate the effect of ambient light on the measurements.

Performance evaluation of receive-diversity free-space optical communications over correlated Gamma-Gamma fading channels.

The efficacy of spatial diversity in practical free-space optical communication systems is impaired by the fading correlation among the underlying subchannels. We consider in this paper the generation of correlated Gamma-Gamma random variables in view of evaluating the system outage probability and bit-error-rate under the condition of correlated fading. Considering the case of receive-diversity systems with intensity modulation and direct detection, we propose a set of criteria for setting the correlation coefficients on the small- and large-scale fading components based on scintillation theory. We verify these criteria using wave-optics simulations and further show through Monte Carlo simulations that we can effectively neglect the correlation corresponding to the small-scale turbulence in most practical systems, irrespective of the specific turbulence conditions. This has not been clarified before, to the best of our knowledge. We then present some numerical results to illustrate the effect of fading correlation on the system performance. Our conclusions can be generalized to the cases of multiple-beam and multiple-beam multiple-aperture systems.

Efficient time of arrival estimation in the presence of multipath propagation.

Most of acoustical experiments face multipath propagation issues. The times of arrival of different ray paths on a sensor can be very close. To estimate them, high resolution algorithms have been developed. The main drawback of these methods is their need of a full rank spectral matrix of the signals. The frequential smoothing technique overcomes this issue by dividing the received signal spectrum into several overlapping sub-bands. This division yields a transfer matrix that may suffer rank deficiency. In this paper, a new criterion to optimally choose the sub-band frequencies is proposed. Encouraging results were obtained on real-world data.

Bayesian method with spatial constraint for retinal vessel segmentation.

A Bayesian method with spatial constraint is proposed for vessel segmentation in retinal images. The proposed model makes the assumption that the posterior probability of each pixel is dependent on posterior probabilities of their neighboring pixels. An energy function is defined for the proposed model. By applying the modified level set approach to minimize the proposed energy function, we can identify blood vessels in the retinal image. Evaluation of the developed method is done on real retinal images which are from the DRIVE database and the STARE database. The performance is analyzed and compared to other published methods using a number of measures which include accuracy, sensitivity, and specificity. The proposed approach is proved to be effective on these two databases. The average accuracy, sensitivity, and specificity on the DRIVE database are 0.9529, 0.7513, and 0.9792, respectively, and for the STARE database 0.9476, 0.7147, and 0.9735, respectively. The performance is better than that of other vessel segmentation methods.

Automatic segmentation and measurement of vasculature in retinal fundus images using probabilistic formulation.

The automatic analysis of retinal blood vessels plays an important role in the computer-aided diagnosis. In this paper, we introduce a probabilistic tracking-based method for automatic vessel segmentation in retinal images. We take into account vessel edge detection on the whole retinal image and handle different vessel structures. During the tracking process, a Bayesian method with maximum a posteriori (MAP) as criterion is used to detect vessel edge points. Experimental evaluations of the tracking algorithm are performed on real retinal images from three publicly available databases: STARE (Hoover et al., 2000), DRIVE (Staal et al., 2004), and REVIEW (Al-Diri et al., 2008 and 2009). We got high accuracy in vessel segmentation, width measurements, and vessel structure identification. The sensitivity and specificity on STARE are 0.7248 and 0.9666, respectively. On DRIVE, the sensitivity is 0.6522 and the specificity is up to 0.9710.

Visually inspecting specular surfaces: A generalized image capture and image description approach.

Image capturing and image content description can be regarded as the two major steps of a computer vision process. This paper focuses on both within the field of specular surface inspection, by generalizing a previously defined stripebased inspection method to free-form surfaces on the basis of a specific stripe illumination technique and by outlining a general feature-based stripe image characterization approach by means of new theoretical concepts. One major purpose of this paper is to propose a general stripe image interpretation approach on the basis of a three-step procedure: 1) comparison of different image content description techniques, 2) fusion of the most appropriate ones, and 3) selection of the optimal features. It is shown that this approach leads to an increase in the classification rates of more than 2 percent between the initial fused set and the selected one. The new contributions encompass 1) the generalization of a cylindrical specular surface enhancement technique to more complex specular geometries, 2) the generalization of the previously defined stripe image description by using the same number of features for the bright and the dark stripes, and 3) the definition of an optimal, in terms of classification rates and computational costs, stripe feature set.

Channel coding and time-diversity for optical wireless links.

Atmospheric turbulence can cause a significant performance degradation in free space optical communication systems. An efficient solution could be to exploit the temporal diversity to improve the performance of the transmission link. Depending on the tolerable delay latency, we can benefit from some degree of time diversity that we can exploit by employing channel coding and interleaving. In this paper, we investigate the efficiency of several channel coding techniques for different time diversity orders and turbulence conditions. We show that a simple convolutional code is a suitable choice in most cases as it makes a good compromise between decoding complexity and performance. We also study the receiver performance when the channel is estimated based on some training symbols.

Fast approximation of transfer cross coefficient for optical proximity correction.

Model Based Optical Proximity Correction (MBOPC) is since a decade a widely used technique that permits to achieve resolutions on silicon layout smaller than the wavelength used in commercially-available photolithography tools. This is an important point, because patterns dimensions on masks are continuously shrinking. Commonly-used algorithms, involving Transfer Cross Coefficients (TCC) drawn from Hopkins formulation to compute aerial images during MBOPC treatment are based on TCC decomposition into its eigenvectors using matricization and the well known Singular Value Decomposition (SVD) tool. This technique remains highly runtime consuming. We propose in this paper to extend a fast fixed point algorithm to estimate an a priori fixed number of leading eigenvectors required to obtain a good approximation while ensuring a low information loss for computing aerial images.

Subspace-based and DIRECT algorithms for distorted circular contour estimation.

Circular features are commonly sought in digital image processing. The subspace-based line detection (SLIDE) method proposed to estimate the center and the radius of a single circle. In this paper, we introduce a novel method for estimating several radii while extending the circle estimation to retrieve circular-like distorted contours. Particularly, we develop and validate a new model for virtual signal generation by simulating a circular antenna. The circle center is estimated by the SLIDE method. A variable speed propagation scheme toward the circular antenna yields a linear phase signal. Therefore, a high-resolution method provides the radius. Either the gradient method or the more robust combination of dividing rectangles and spline interpolation can extend this method extend this method for free form object segmentation. The retrieval of multiple non concentric circles and rotated ellipses is also considered. To evaluate the performance of the proposed methods, we compare them with a least-squares method, Hough transform, and gradient vector flow. We apply the proposed method to hand-made images while considering some real-world images.