Face recognition based on curvelets, invariant moments features and SVM

Recent studies highlighted on face recognition methods. In this paper, a new algorithm is proposed for face recognition by combining Fast Discrete Curvelet Transform (FDCvT) and Invariant Moments with Support vector machine (SVM), which improves rate of face recognition in various situations. The reason of using this approach depends on two things. first, Curvelet transform which is a multi-resolution method, that can efficiently represent image edge discontinuities; Second, the Invariant Moments analysis which is a statistical method that meets with the translation, rotation and scale invariance in the image. Furthermore, SVM is employed to classify the face image based on the extracted features. This process is applied on each of ORL and Yale databases to evaluate the performance of the suggested method. Experimentally, the proposed method results show that our system can compose efficient and reasonable face recognition feature, and obtain useful recognition accuracy, which is able to face and side-face states detection of persons to decrease fault rate of production

Random Image Matching CAPTCHA System

Security risks is an important issues and caught the attention of researchers in the area of networks, web development, human computer interaction and software engineering. One main challenge for online systems is to identify whether the users are humans or software robots (bots). While it is natural to provide service to human users, providing service for software robots (bots) comes with many security risks and challenges. Software robots are often used by spammers to create fake online accounts, affect search engine ranking, take part in on-line polls, send out spam or simply waste the resources of the server. In this paper we introduce a visual CAPTCHA technique that is based on generating random images by the computer, theuser is then asked to match a feature point between two images (i.e. solve the correspondence problem as defined by the researchers in the computer vision area). The relationship between the two images is based on a randomly generated homography transformation function. The main advantage of our approach compared to other visual CAPTCHA techniques is that we eliminate the need for a database of images while retaining ease of use

The detection of handguns from live-video in real-time based on deep learning

Many people have been killed indiscriminately by the use of handguns in different countries. Terroristacts, online fighting games and mentally disturbed people are considered the common reasons for these crimes.  A real-time handguns detection surveillance system is built to overcome these badacts, based on convolutional neural networks (CNNs). This method is focused on the detection of different weapons, such as (handgun and rifles). The identification of handguns from surveillance cameras and images requires monitoring by human supervisor, that can cause errors. To overcome this issue,the designed detection system sends an alert message to the supervisor when aweapon is detected. In the proposed detection system, a pre-trained deep learning model MobileNetV3-SSDLite is used to perform the handgundetection operation. This model has been selected becauseit is fast and accurate in infering to integrate network for detecting and classifying weaponsin images. The experimental result using global handguns datasets of various weapons showed that the use of MobileNetV3 with SSDLite model bothenhance the accuracy level in identifying the real time handguns detection

Video object extraction in distributed surveillance systems

Recently, automated video surveillance and related video processing algorithms have received considerable attention from the research community. Challenges in video surveillance rise from noise, illumination changes, camera motion, splits and occlusions, complex human behavior, and how to manage extracted surveillance information for delivery, archiving, and retrieval: Many video surveillance systems focus on video object extraction, while few focus on both the system architecture and video object extraction. We focus on both and integrate them to produce an end-to-end system and study the challenges associated with building this system. We propose a scalable, distributed, and real-time video-surveillance system with a novel architecture, indexing, and retrieval. The system consists of three modules: video workstations for processing, control workstations for monitoring, and a server for management and archiving. The proposed system models object features as temporal Gaussians and produces: an 18 frames/second frame-rate for SIF video and static cameras, reduced network and storage usage, and precise retrieval results. It is more scalable and delivers more balanced distributed performance than recent architectures. The first stage of video processing is noise estimation. We propose a method for localizing homogeneity and estimating the additive white Gaussian noise variance, which uses spatially scattered initial seeds and utilizes particle filtering techniques to guide their spatial movement towards homogeneous locations from which the estimation is performed. The noise estimation method reduces the number of measurements required by block-based methods while achieving more accuracy. Next, we segment video objects using a background subtraction technique. We generate the background model online for static cameras using a mixture of Gaussians background maintenance approach. For moving cameras, we use a global motion estimation method offline to bring neighboring frames into the coordinate system of the current frame and we merge them to produce the background model. We track detected objects using a feature-based object tracking method with improved detection and correction of occlusion and split. We detect occlusion and split through the identification of sudden variations in the spatia-temporal features of objects. To detect splits, we analyze the temporal behavior of split objects to discriminate between errors in segmentation and real separation of objects. Both objective and subjective experimental results show the ability of the proposed algorithm to detect and correct both splits and occlusions of objects. For the last stage of video processing, we propose a novel method for the detection of vandalism events which is based on a proposed definition for vandal behaviors recorded on surveillance video sequences. We monitor changes inside a restricted site containing vandalism-prone objects and declare vandalism when an object is detected as leaving the site while there is temporally consistent and significant static changes representing damage, given that the site is normally unchanged after use. The proposed method is tested on sequences showing real and simulated vandal behaviors and it achieves a detection rate of 96%. It detects different forms of vandalism such as graffiti and theft. The proposed end-ta-end video surveillance system aims at realizing the potential of video object extraction in automated surveillance and retrieval by focusing on both video object extraction and the management, delivery, and utilization of the extracted informatio

Structure-oriented directional approaches to video noise estimation and reduction

Video has become increasingly used in television broadcast, Internet, and surveillance applications. The presence of noise in video signals is not only visually unacceptable, but also hinders the performance of video processing applications. Thus, the interest in researching methods for fast, automated, and robust techniques to estimate and reduce image and video noise has grown over the years. This thesis proposes approaches to estimate and reduce additive white Gaussian noise (AWGN) in video signals that are adaptive to frame structure and noise level. First, a spatio-temporal method for estimating the variance of AWGN is proposed. The method divides the video signal into cubes. Cube homogeneity is measured using Laplacian of Gaussian operators. The variances of homogeneous cubes calculated along homogeneous plains are used to estimate the noise variance. The Least Median of Squares (LMS) robust estimator is utilized to reject outliers and produce the domain-wise noise variance estimate. The domain-wise estimates are averaged to obtain the frame-wise estimate. The proposed algorithm works well for video sequences with high structure and motion activity with a maximum estimation error of 1.7 dB. The thesis then proposes a framework for spatial adaptive multi-directional filtering of AWGN in video frames and adaptive multi-directional Sigma and Wiener filters. The proposed multi-directional Sigma filter achieves gains in the Peak Signal to Noise Ratio (PSNR) of up to 4.8 dB in real-time. The proposed multi-directional Wiener filter achieves gains in PSNR of up to 5.6 dB and is well suited for offline applications. The structure preservation capabilities of the proposed filters are studied using the Modulation Transfer Functio

Modeling of New Single-Phase High Voltage Power Supply for Industrial Microwave Generators for N=2 Magnetrons

In this paper, we propose to study the modeling and the simulation, using the EMTP code, of the operation in nominal mode of a new high voltage power device, based on the same use principle of an only transformer with shunts, but this time for several magnetrons. This modeling can lead to the determination of the construction parameters of the new transformer, to ensure the correct operation in nominal mode of the new power supply, while respecting the control criterion of the current in each magnetron. The knowledge of these parameters (size of the magnetic circuit, the magnetic quality, the air gap, the size of the shunt and the number of turns, etc .....), determines the cost of the dimensioned transformer for its realization. In the following, we will treat all possible states for operation of this new power supply with two magnetrons (Power supply for two magnetrons: case of two magnetrons in service, case of one magnetron in service and one magnetron in failure, case of tow magnetrons in failure). This provides relative to the current device gains of space, volume, cost of implementation and maintenance.DOI:http://dx.doi.org/10.11591/ijece.v4i2.568

A comparison between google cloud service and icloud

© 2019 IEEE. The availability of high speed networks and low cost storage devices and computer in addition to the adoption of Service-Oriented-Architecture has opened the door to Cloud Storage Services by many providers. During the recent couple of years, more companies are moving towards cloud storage due to the many reasons such as: scalable on demand disk storage space, backup and data replication and the ability to share and access data from anywhere and anytime. The main objective of the paper is to compare different kinds of Cloud storage service providers. It starts by offering a brief introduction to cloud storage followed by an outline of the general history of cloud services and then moves on to the specific history of two major cloud services: iCloud and Google cloud platform. Furthermore, the various features of the two cloud services are explored and a comparison is made between them

Descriptive Epidemiological study of COVID-19 in Maghreb and European countries

An outbreak of 2019 novel coronavirus diseases (COVID-19) has spread quickly world wide. We performed a descriptive epidemiological study of COVID-19 in Mediterranean North African countries and South European countries. Cases data were collected through May 3, 2020 from WHO. Data analysis included: 1) geo-temporal analysis of viral spread in 6 countries (Morocco, Algeria, Tunisia, Spain, France and Italy), 2) epidemiological curve construction, 3)mortality and cured rates, 4) study  factors that led to differences of the spread of the virus in these 6 countries, and 5) comparison between Morocco and three European countries. The number of infected cases between North African and Southern European countries were different, which might be related to restriction conditions, age, geographic location, and lifestyle. We observed that The COVID-19 epidemic has spread very quickly in Southern European compared to North African countrie

Deep Learning Based Method for Computer Aided Diagnosis of Diabetic Retinopathy

© 2019 IEEE. Diabetic retinopathy (DR) is a retinal disease caused by the high blood sugar levels that may damage and block the blood vessels feeding the retina. In the early stages of DR, the disease is asymptomatic; however, as the disease advances, a possible sudden loss of vision and blindness may occur. Therefore, an early diagnosis and staging of the disease is required to possibly slow down the progression of the disease and improve control of the symptoms. In response to the previous challenge, we introduce a computer aided diagnosis tool based on convolutional neural networks (CNN) to classify fundus images into one of the five stages of DR. The proposed CNN consists of a preprocessing stage, five stage convolutional, rectified linear and pooling layers followed by three fully connected layers. Transfer learning was adopted to minimize overfitting by training the model on a larger dataset of 3.2 million images (i.e. ImageNet) prior to the use of the model on the APTOS 2019 Kaggle DR dataset. The proposed approach has achieved a testing accuracy of 77% and a quadratic weighted kappa score of 78%, offering a promising solution for a successful early diagnose and staging of DR in an automated fashion