Search results for: detection task
2209 Concealed Objects Detection in Visible, Infrared and Terahertz Ranges
Authors: M. Kowalski, M. Kastek, M. Szustakowski
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Multispectral screening systems are becoming more popular because of their very interesting properties and applications. One of the most significant applications of multispectral screening systems is prevention of terrorist attacks. There are many kinds of threats and many methods of detection. Visual detection of objects hidden under clothing of a person is one of the most challenging problems of threats detection. There are various solutions of the problem; however, the most effective utilize multispectral surveillance imagers. The development of imaging devices and exploration of new spectral bands is a chance to introduce new equipment for assuring public safety. We investigate the possibility of long lasting detection of potentially dangerous objects covered with various types of clothing. In the article we present the results of comparative studies of passive imaging in three spectrums – visible, infrared and terahertz.
Keywords: Infrared, image processing, object detection, screening camera, terahertz.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 30862208 Adaptive Network Intrusion Detection Learning: Attribute Selection and Classification
Authors: Dewan Md. Farid, Jerome Darmont, Nouria Harbi, Nguyen Huu Hoa, Mohammad Zahidur Rahman
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In this paper, a new learning approach for network intrusion detection using naïve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datasets are dynamic, complex and contain large number of attributes. Some of the attributes may be redundant or contribute little for detection making. It has been successfully tested that significant attribute selection is important to design a real world intrusion detection systems (IDS). The purpose of this study is to identify effective attributes from the training dataset to build a classifier for network intrusion detection using data mining algorithms. The experimental results on KDD99 benchmark intrusion detection dataset demonstrate that this new approach achieves high classification rates and reduce false positives using limited computational resources.Keywords: Attributes selection, Conditional probabilities, information gain, network intrusion detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26822207 Shot Boundary Detection Using Octagon Square Search Pattern
Authors: J. Kavitha, S. Sowmyayani, P. Arockia Jansi Rani
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In this paper, a shot boundary detection method is presented using octagon square search pattern. The color, edge, motion and texture features of each frame are extracted and used in shot boundary detection. The motion feature is extracted using octagon square search pattern. Then, the transition detection method is capable of detecting the shot or non-shot boundaries in the video using the feature weight values. Experimental results are evaluated in TRECVID video test set containing various types of shot transition with lighting effects, object and camera movement within the shots. Further, this paper compares the experimental results of the proposed method with existing methods. It shows that the proposed method outperforms the state-of-art methods for shot boundary detection.
Keywords: Content-based indexing and retrieval, cut transition detection, discrete wavelet transform, shot boundary detection, video source.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9932206 An Example of Open Robot Controller Architecture - For Power Distribution Line Maintenance Robot System -
Authors: Yingxin He, Kyouichi Tatsuno
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In this paper, we propose an architecture for easily constructing a robot controller. The architecture is a multi-agent system which has eight agents: the Man-machine interface, Task planner, Task teaching editor, Motion planner, Arm controller, Vehicle controller, Vision system and CG display. The controller has three databases: the Task knowledge database, the Robot database and the Environment database. Based on this controller architecture, we are constructing an experimental power distribution line maintenance robot system and are doing the experiment for the maintenance tasks, for example, “Bolt insertion task".Keywords: Robot controller, Software library, Maintenance robot, Robot language, Agent system.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13912205 Use of Hierarchical Temporal Memory Algorithm in Heart Attack Detection
Authors: Tesnim Charrad, Kaouther Nouira, Ahmed Ferchichi
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In order to reduce the number of deaths due to heart problems, we propose the use of Hierarchical Temporal Memory Algorithm (HTM) which is a real time anomaly detection algorithm. HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work. It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies. Experiments showed good performance in terms of specificity, sensitivity and execution time.Keywords: HTM, Real time anomaly detection, ECG, Cardiac Anomalies.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7792204 Intrusion Detection System Based On The Integrity of TCP Packet
Authors: Moad Alhamaty , Ali Yazdian , Fathi Al-qadasi
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A common way to elude the signature-based Network Intrusion Detection System is based upon changing a recognizable attack to an unrecognizable one via the IDS. For example, in order to evade sign accommodation with intrusion detection system markers, a hacker spilt the payload packet into many small pieces or hides them within messages. In this paper we try to model the main fragmentation attack and create a new module in the intrusion detection architecture system which recognizes the main fragmentation attacks through verification of integrity checking of TCP packet in order to prevent elusion of the system and also to announce the necessary alert to the system administrator.
Keywords: Intrusion detection system, Evasion techniques, Fragmentation attacks, TCP Packet integrity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18392203 Identifying Attack Code through an Ontology-Based Multiagent Tool: FROID
Authors: Salvador Mandujano
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This paper describes the design and results of FROID, an outbound intrusion detection system built with agent technology and supported by an attacker-centric ontology. The prototype features a misuse-based detection mechanism that identifies remote attack tools in execution. Misuse signatures composed of attributes selected through entropy analysis of outgoing traffic streams and process runtime data are derived from execution variants of attack programs. The core of the architecture is a mesh of self-contained detection cells organized non-hierarchically that group agents in a functional fashion. The experiments show performance gains when the ontology is enabled as well as an increase in accuracy achieved when correlation cells combine detection evidence received from independent detection cells.Keywords: Outbound intrusion detection, knowledge management, multiagent systems, ontology.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16522202 The Comparation of Limits of Detection of Lateral Flow Immunochromatographic Strips of Different Types of Mycotoxins
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Mycotoxins are secondary metabolic products of fungi. These are poisonous, carcinogens and mutagens in nature and pose a serious health threat to both humans and animals, causing severe illnesses and even deaths. The rapid, simple and cheap detection methods of mycotoxins are of immense importance and in great demand in the food and beverage industry as well as in agriculture and environmental monitoring. Lateral flow immunochromatographic strips (ICSTs) have been widely used in food safety, environment monitoring. 46 papers were identified and reviewed on Google Scholar and Scopus for their limit of detection and nanomaterial on Lateral flow ICSTs on different types of mycotoxins. The papers were dated 2001-2021. 25 papers were compared to identify the lowest limit of detection of among different mycotoxins (Aflatoxin B1: 10, Zearalenone: 5, Fumonisin B1: 5, Trichothecene-A: 5). Most of these highly sensitive strips are competitive. Sandwich structures are usually used in large scale detection. In conclusion, the limit of detection of Aflatoxin B1 is the lowest among these mycotoxins. Gold-nanoparticle based immunochromatographic test strips have the lowest limit of detection. Five papers involve smartphone detection and they all detect aflatoxin B1 with gold nanoparticles.
Keywords: Aflatoxin B1, limit of detection, gold nanoparticle, lateral flow immunochromatographic strips, mycotoxins, smartphone.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3972201 Spatio-Temporal Video Slice Edges Analysis for Shot Transition Detection and Classification
Authors: Aissa Saoudi, Hassane Essafi
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In this work we will present a new approach for shot transition auto-detection. Our approach is based on the analysis of Spatio-Temporal Video Slice (STVS) edges extracted from videos. The proposed approach is capable to efficiently detect both abrupt shot transitions 'cuts' and gradual ones such as fade-in, fade-out and dissolve. Compared to other techniques, our method is distinguished by its high level of precision and speed. Those performances are obtained due to minimizing the problem of the boundary shot detection to a simple 2D image partitioning problem.Keywords: Boundary shot detection, Shot transition detection, Video analysis, Video indexing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16262200 Pain and Lumbar Muscle Activation before and after Functional Task in Nonspecific Chronic Low Back Pain
Authors: Lídia E. O. Cruz, Adriano P. C. Calvo, Renato J. Soares, Regiane A. Carvalho
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Individuals with non-specific chronic low back pain may present altered movement patterns during functional activities. However, muscle behavior before and after performing a functional task with different load conditions is not yet fully understood. The aim of this study is to analyze lumbar muscle activity before and after performing the functional task of picking up and placing an object on the ground (with and without load) in individuals with nonspecific chronic low back pain. 20 subjects with nonspecific chronic low back pain and 20 healthy subjects participated in this study. A surface electromyography was performed in the ilio-costal, longissimus and multifidus muscles to evaluate lumbar muscle activity before and after performing the functional task of picking up and placing an object on the ground, with and without load. The symptomatic participants had greater lumbar muscle activation compared to the asymptomatic group, more evident in performing the task without load, with statistically significant difference (p = 0,033) between groups for the right multifidus muscle. This study showed that individuals with nonspecific chronic low back pain have higher muscle activation before and after performing a functional task compared to healthy participants.
Keywords: Chronic low back pain, functional task, lumbar muscles, muscle activity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5102199 Efficient Iterative Detection Technique in Wireless Communication System
Authors: Hwan-Jun Choi, Sung-Bok Choi, Hyoung-Kyu Song
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Recently, among the MIMO-OFDM detection techniques, a lot of papers suggested V-BLAST scheme which can achieve high data rate. Therefore, the signal detection of MIMO-OFDM system is important issue. In this paper, efficient iterative V-BLAST detection technique is proposed in wireless communication system. The proposed scheme adjusts the number of candidate symbol and iterative scheme based on channel state. According to the simulation result, the proposed scheme has better BER performance than conventional schemes and similar BER performance of the QRD-M with iterative scheme. Moreover complexity of proposed scheme has 50.6% less than complexity of QRD-M detection with iterative scheme. Therefore the proposed detection scheme can be efficiently used in wireless communication.
Keywords: MIMO-OFDM, V-BLAST, QR-decomposition, QRD-M, DFE, Iterative scheme, Channel condition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20472198 Real Time Video Based Smoke Detection Using Double Optical Flow Estimation
Authors: Anton Stadler, Thorsten Ike
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In this paper, we present a video based smoke detection algorithm based on TVL1 optical flow estimation. The main part of the algorithm is an accumulating system for motion angles and upward motion speed of the flow field. We optimized the usage of TVL1 flow estimation for the detection of smoke with very low smoke density. Therefore, we use adapted flow parameters and estimate the flow field on difference images. We show in theory and in evaluation that this improves the performance of smoke detection significantly. We evaluate the smoke algorithm using videos with different smoke densities and different backgrounds. We show that smoke detection is very reliable in varying scenarios. Further we verify that our algorithm is very robust towards crowded scenes disturbance videos.Keywords: Low density, optical flow, upward smoke motion, video based smoke detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14062197 The Correlation between Users’ Star Rating and Usability on Mobile Applications
Authors: Abdulmohsen A. AlBesher, Richard T. Stone
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Star rating for mobile applications is a very useful way to differentiate between the best and worst rated applications. However, the question is whether the rating reflects the level of usability or not. The aim of this paper is to find out if the user’ star ratings on mobile apps correlate with the usability of those apps. Thus, we tested three mobile apps, which have different star ratings: low, medium, and high. Participating in the study, 15 mobile phone users were asked to do one single task for each of the three tested apps. After each task, the participant evaluated the app by answering a survey based on the System Usability Scale (SUS). The results found that there is no major correlation between the star rating and the usability. However, it was found that the task completion time and the numbers of errors that may happen while completing the task were significantly correlated to the usability.Keywords: Mobile applications, SUS, Star rating, Usability.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9072196 On the outlier Detection in Nonlinear Regression
Authors: Hossein Riazoshams, Midi Habshah, Jr., Mohamad Bakri Adam
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The detection of outliers is very essential because of their responsibility for producing huge interpretative problem in linear as well as in nonlinear regression analysis. Much work has been accomplished on the identification of outlier in linear regression, but not in nonlinear regression. In this article we propose several outlier detection techniques for nonlinear regression. The main idea is to use the linear approximation of a nonlinear model and consider the gradient as the design matrix. Subsequently, the detection techniques are formulated. Six detection measures are developed that combined with three estimation techniques such as the Least-Squares, M and MM-estimators. The study shows that among the six measures, only the studentized residual and Cook Distance which combined with the MM estimator, consistently capable of identifying the correct outliers.Keywords: Nonlinear Regression, outliers, Gradient, LeastSquare, M-estimate, MM-estimate.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 31642195 Detection of Moving Images Using Neural Network
Authors: P. Latha, L. Ganesan, N. Ramaraj, P. V. Hari Venkatesh
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Motion detection is a basic operation in the selection of significant segments of the video signals. For an effective Human Computer Intelligent Interaction, the computer needs to recognize the motion and track the moving object. Here an efficient neural network system is proposed for motion detection from the static background. This method mainly consists of four parts like Frame Separation, Rough Motion Detection, Network Formation and Training, Object Tracking. This paper can be used to verify real time detections in such a way that it can be used in defense applications, bio-medical applications and robotics. This can also be used for obtaining detection information related to the size, location and direction of motion of moving objects for assessment purposes. The time taken for video tracking by this Neural Network is only few seconds.
Keywords: Frame separation, Correlation Network, Neural network training, Radial Basis Function, object tracking, Motion Detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 31362194 Puff Noise Detection and Cancellation for Robust Speech Recognition
Authors: Sangjun Park, Jungpyo Hong, Byung-Ok Kang, Yun-keun Lee, Minsoo Hahn
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In this paper, an algorithm for detecting and attenuating puff noises frequently generated under the mobile environment is proposed. As a baseline system, puff detection system is designed based on Gaussian Mixture Model (GMM), and 39th Mel Frequency Cepstral Coefficient (MFCC) is extracted as feature parameters. To improve the detection performance, effective acoustic features for puff detection are proposed. In addition, detected puff intervals are attenuated by high-pass filtering. The speech recognition rate was measured for evaluation and confusion matrix and ROC curve are used to confirm the validity of the proposed system.Keywords: Gaussian mixture model, puff detection and cancellation, speech enhancement.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22222193 Structural Damage Detection Using Sensors Optimally Located
Authors: Carlos Alberto Riveros, Edwin Fabián García, Javier Enrique Rivero
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The measured data obtained from sensors in continuous monitoring of civil structures are mainly used for modal identification and damage detection. Therefore, when modal identification analysis is carried out the quality in the identification of the modes will highly influence the damage detection results. It is also widely recognized that the usefulness of the measured data used for modal identification and damage detection is significantly influenced by the number and locations of sensors. The objective of this study is the numerical implementation of two widely known optimum sensor placement methods in beam-like structures.
Keywords: Optimum sensor placement, structural damage detection, modal identification, beam-like structures.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22812192 Active Islanding Detection Method Using Intelligent Controller
Authors: Kuang-Hsiung Tan, Chih-Chan Hu, Chien-Wu Lan, Shih-Sung Lin, Te-Jen Chang
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An active islanding detection method using disturbance signal injection with intelligent controller is proposed in this study. First, a DC\AC power inverter is emulated in the distributed generator (DG) system to implement the tracking control of active power, reactive power outputs and the islanding detection. The proposed active islanding detection method is based on injecting a disturbance signal into the power inverter system through the d-axis current which leads to a frequency deviation at the terminal of the RLC load when the utility power is disconnected. Moreover, in order to improve the transient and steady-state responses of the active power and reactive power outputs of the power inverter, and to further improve the performance of the islanding detection method, two probabilistic fuzzy neural networks (PFNN) are adopted to replace the traditional proportional-integral (PI) controllers for the tracking control and the islanding detection. Furthermore, the network structure and the online learning algorithm of the PFNN are introduced in detail. Finally, the feasibility and effectiveness of the tracking control and the proposed active islanding detection method are verified with experimental results.
Keywords: Distributed generators, probabilistic fuzzy neural network, islanding detection, non-detection zone.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14112191 Vehicle Detection Method using Haar-like Feature on Real Time System
Authors: Sungji Han, Youngjoon Han, Hernsoo Hahn
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This paper presents a robust vehicle detection approach using Haar-like feature. It is possible to get a strong edge feature from this Haar-like feature. Therefore it is very effective to remove the shadow of a vehicle on the road. And we can detect the boundary of vehicles accurately. In the paper, the vehicle detection algorithm can be divided into two main steps. One is hypothesis generation, and the other is hypothesis verification. In the first step, it determines vehicle candidates using features such as a shadow, intensity, and vertical edge. And in the second step, it determines whether the candidate is a vehicle or not by using the symmetry of vehicle edge features. In this research, we can get the detection rate over 15 frames per second on our embedded system.
Keywords: vehicle detection, haar-like feauture, single camera, real time
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 33222190 An Edit-Distance Algorithm to Detect Correlated Attacks in Distributed Systems
Authors: Sule Simsek
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Intrusion detection systems (IDS)are crucial components of the security mechanisms of today-s computer systems. Existing research on intrusion detection has focused on sequential intrusions. However, intrusions can also be formed by concurrent interactions of multiple processes. Some of the intrusions caused by these interactions cannot be detected using sequential intrusion detection methods. Therefore, there is a need for a mechanism that views the distributed system as a whole. L-BIDS (Lattice-Based Intrusion Detection System) is proposed to address this problem. In the L-BIDS framework, a library of intrusions and distributed traces are represented as lattices. Then these lattices are compared in order to detect intrusions in the distributed traces.Keywords: Attack graph, distributed, edit-distance, misuse detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13762189 Wavelet Entropy Based Algorithm for Fault Detection and Classification in FACTS Compensated Transmission Line
Authors: Amany M. El-Zonkoly, Hussein Desouki
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Distance protection of transmission lines including advanced flexible AC transmission system (FACTS) devices has been a very challenging task. FACTS devices of interest in this paper are static synchronous series compensators (SSSC) and unified power flow controller (UPFC). In this paper, a new algorithm is proposed to detect and classify the fault and identify the fault position in a transmission line with respect to a FACTS device placed in the midpoint of the transmission line. Discrete wavelet transformation and wavelet entropy calculations are used to analyze during fault current and voltage signals of the compensated transmission line. The proposed algorithm is very simple and accurate in fault detection and classification. A variety of fault cases and simulation results are introduced to show the effectiveness of such algorithm.
Keywords: Entropy calculation, FACTS, SSSC, UPFC, wavelet transform.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20612188 A Moving Human-Object Detection for Video Access Monitoring
Authors: Won-Ho Kim, Nuwan Sanjeewa Rajasooriya
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In this paper, a simple moving human detection method is proposed for video surveillance system or access monitoring system. The frame difference and noise threshold are used for initial detection of a moving human-object, and simple labeling method is applied for final human-object segmentation. The simulated results show that the applied algorithm is fast to detect the moving human-objects by performing 95% of correct detection rate. The proposed algorithm has confirmed that can be used as an intelligent video access monitoring system.
Keywords: Moving human-object detection, Video access monitoring, Image processing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24952187 Radar Task Schedulers based on Multiple Queue
Authors: María I. Jiménez, Alberto Izquierdo, Juan J. Villacorta, Lara del Val, Mariano Raboso
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There are very complex communication systems, as the multifunction radar, MFAR (Multi-Function Array Radar), where functions are integrated all together, and simultaneously are performed the classic functions of tracking and surveillance, as all the functions related to the communication, countermeasures, and calibration. All these functions are divided into the tasks to execute. The task scheduler is a key element of the radar, since it does the planning and distribution of energy and time resources to be shared and used by all tasks. This paper presents schedulers based on the use of multiple queue. Several schedulers have been designed and studied, and it has been made a comparative analysis of different performed schedulers. The tests and experiments have been done by means of system software simulation. Finally a suitable set of radar characteristics has been selected to evaluate the behavior of the task scheduler working.Keywords: Queue Theory, Radar, Scheduler, Task.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21842186 Detecting Defects in Textile Fabrics with Optimal Gabor Filters
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This paper investigates the problem of automated defect detection for textile fabrics and proposes a new optimal filter design method to solve this problem. Gabor Wavelet Network (GWN) is chosen as the major technique to extract the texture features from textile fabrics. Based on the features extracted, an optimal Gabor filter can be designed. In view of this optimal filter, a new semi-supervised defect detection scheme is proposed, which consists of one real-valued Gabor filter and one smoothing filter. The performance of the scheme is evaluated by using an offline test database with 78 homogeneous textile images. The test results exhibit accurate defect detection with low false alarm, thus showing the effectiveness and robustness of the proposed scheme. To evaluate the detection scheme comprehensively, a prototyped detection system is developed to conduct a real time test. The experiment results obtained confirm the efficiency and effectiveness of the proposed detection scheme.Keywords: Defect detection, Filtering, Gabor function, Gaborwavelet networks, Textile fabrics.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23392185 Hybrid Intelligent Intrusion Detection System
Authors: Norbik Bashah, Idris Bharanidharan Shanmugam, Abdul Manan Ahmed
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Intrusion Detection Systems are increasingly a key part of systems defense. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Artificial Intelligence plays a driving role in security services. This paper proposes a dynamic model Intelligent Intrusion Detection System, based on specific AI approach for intrusion detection. The techniques that are being investigated includes neural networks and fuzzy logic with network profiling, that uses simple data mining techniques to process the network data. The proposed system is a hybrid system that combines anomaly, misuse and host based detection. Simple Fuzzy rules allow us to construct if-then rules that reflect common ways of describing security attacks. For host based intrusion detection we use neural-networks along with self organizing maps. Suspicious intrusions can be traced back to its original source path and any traffic from that particular source will be redirected back to them in future. Both network traffic and system audit data are used as inputs for both.Keywords: Intrusion Detection, Network Security, Data mining, Fuzzy Logic.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21202184 A Survey on Facial Feature Points Detection Techniques and Approaches
Authors: Rachid Ahdid, Khaddouj Taifi, Said Safi, Bouzid Manaut
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Automatic detection of facial feature points plays an important role in applications such as facial feature tracking, human-machine interaction and face recognition. The majority of facial feature points detection methods using two-dimensional or three-dimensional data are covered in existing survey papers. In this article chosen approaches to the facial features detection have been gathered and described. This overview focuses on the class of researches exploiting facial feature points detection to represent facial surface for two-dimensional or three-dimensional face. In the conclusion, we discusses advantages and disadvantages of the presented algorithms.Keywords: Facial feature points, face recognition, facial feature tracking, two-dimensional data, three-dimensional data.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16712183 Feature Point Detection by Combining Advantages of Intensity-based Approach and Edge-based Approach
Authors: Sungho Kim, Chaehoon Park, Yukyung Choi, Soon Kwon, In So Kweon
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In this paper, a novel corner detection method is presented to stably extract geometrically important corners. Intensity-based corner detectors such as the Harris corner can detect corners in noisy environments but has inaccurate corner position and misses the corners of obtuse angles. Edge-based corner detectors such as Curvature Scale Space can detect structural corners but show unstable corner detection due to incomplete edge detection in noisy environments. The proposed image-based direct curvature estimation can overcome limitations in both inaccurate structural corner detection of the Harris corner detector (intensity-based) and the unstable corner detection of Curvature Scale Space caused by incomplete edge detection. Various experimental results validate the robustness of the proposed method.Keywords: Feature, intensity, contour, hybrid.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18142182 Fault Detection and Isolation in Attitude Control Subsystem of Spacecraft Formation Flying Using Extended Kalman Filters
Authors: S. Ghasemi, K. Khorasani
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In this paper, the problem of fault detection and isolation in the attitude control subsystem of spacecraft formation flying is considered. In order to design the fault detection method, an extended Kalman filter is utilized which is a nonlinear stochastic state estimation method. Three fault detection architectures, namely, centralized, decentralized, and semi-decentralized are designed based on the extended Kalman filters. Moreover, the residual generation and threshold selection techniques are proposed for these architectures.
Keywords: Formation flight of satellites, extended Kalman filter, fault detection and isolation, actuator fault.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19392181 Improved C-Fuzzy Decision Tree for Intrusion Detection
Authors: Krishnamoorthi Makkithaya, N. V. Subba Reddy, U. Dinesh Acharya
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As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents our work to test and improve the performance of a new class of decision tree c-fuzzy decision tree to detect intrusion. The work also includes identifying best candidate feature sub set to build the efficient c-fuzzy decision tree based Intrusion Detection System (IDS). We investigated the usefulness of c-fuzzy decision tree for developing IDS with a data partition based on horizontal fragmentation. Empirical results indicate the usefulness of our approach in developing the efficient IDS.Keywords: Data mining, Decision tree, Feature selection, Fuzzyc- means clustering, Intrusion detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15662180 Towards Integrating Statistical Color Features for Human Skin Detection
Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani
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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.
Keywords: Color space, neural network, random forest, skin detection, statistical feature.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1941