• Title/Summary/Keyword: Detection Techniques

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A Vision-Based Method to Find Fingertips in a Closed Hand

  • Chaudhary, Ankit;Vatwani, Kapil;Agrawal, Tushar;Raheja, J.L.
    • Journal of Information Processing Systems
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    • v.8 no.3
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    • pp.399-408
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    • 2012
  • Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

Real-Time QRS Detection Using Wavelet Packet Transform

  • Bholsithi, Wisarut;;Hinjit, Watcharapong;Dejhan, Kobchai
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.1880-1884
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    • 2004
  • The wavelet packet transform has been applied for QRS detection with squaring, window integration, and impulse filter techniques to cut down the false detection of QRS complex. This real time QRS detection has been performed on Simulink and Matlab. The correct QRS detection rates have reached to 99.75% in the experiment with 15 sets of ECG data from European ST-T database which are kept in Physionet.

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A Study on Attack Detection using Hierarchy Architecture in Mobile Ad Hoc Network (MANET에서 계층 구조를 이용한 공격 탐지 기법 연구)

  • Yang, Hwan Seok
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.10 no.2
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    • pp.75-82
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    • 2014
  • MANET has various types of attacks. In particular, routing attacks using characteristics of movement of nodes and wireless communication is the most threatening because all nodes which configure network perform a function of router which forwards packets. Therefore, mechanisms that detect routing attacks and defense must be applied. In this paper, we proposed hierarchical structure attack detection techniques in order to improve the detection ability against routing attacks. Black hole detection is performed using PIT for monitoring about control packets within cluster and packet information management on the cluster head. Flooding attack prevention is performed using cooperation-based distributed detection technique by member nodes. For this, member node uses NTT for information management of neighbor nodes and threshold whether attack or not receives from cluster head. The performance of attack detection could be further improved by calculating at regular intervals threshold considering the total traffic within cluster in the cluster head.

Trends on Object Detection Techniques Based on Deep Learning (딥러닝 기반 객체 인식 기술 동향)

  • Lee, J.S.;Lee, S.K.;Kim, D.W.;Hong, S.J.;Yang, S.I.
    • Electronics and Telecommunications Trends
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    • v.33 no.4
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    • pp.23-32
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    • 2018
  • Object detection is a challenging field in the visual understanding research area, detecting objects in visual scenes, and the location of such objects. It has recently been applied in various fields such as autonomous driving, image surveillance, and face recognition. In traditional methods of object detection, handcrafted features have been designed for overcoming various visual environments; however, they have a trade-off issue between accuracy and computational efficiency. Deep learning is a revolutionary paradigm in the machine-learning field. In addition, because deep-learning-based methods, particularly convolutional neural networks (CNNs), have outperformed conventional methods in terms of object detection, they have been studied in recent years. In this article, we provide a brief descriptive summary of several recent deep-learning methods for object detection and deep learning architectures. We also compare the performance of these methods and present a research guide of the object detection field.

Feature Selection Algorithms in Intrusion Detection System: A Survey

  • MAZA, Sofiane;TOUAHRIA, Mohamed
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.10
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    • pp.5079-5099
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    • 2018
  • Regarding to the huge number of connections and the large flow of data on the Internet, Intrusion Detection System (IDS) has a difficulty to detect attacks. Moreover, irrelevant and redundant features influence on the quality of IDS precisely on the detection rate and processing cost. Feature Selection (FS) is the important technique, which gives the issue for enhancing the performance of detection. There are different works have been proposed, but a map for understanding and constructing a state of the FS in IDS is still need more investigation. In this paper, we introduce a survey of feature selection algorithms for intrusion detection system. We describe the well-known approaches that have been proposed in FS for IDS. Furthermore, we provide a classification with a comparative study between different contribution according to their techniques and results. We identify a new taxonomy for future trends and existing challenges.

Enhanced Network Intrusion Detection using Deep Convolutional Neural Networks

  • Naseer, Sheraz;Saleem, Yasir
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.10
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    • pp.5159-5178
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    • 2018
  • Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.

Nonlinear damage detection using higher statistical moments of structural responses

  • Yu, Ling;Zhu, Jun-Hua
    • Structural Engineering and Mechanics
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    • v.54 no.2
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    • pp.221-237
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    • 2015
  • An integrated method is proposed for structural nonlinear damage detection based on time series analysis and the higher statistical moments of structural responses in this study. It combines the time series analysis, the higher statistical moments of AR model residual errors and the fuzzy c-means (FCM) clustering techniques. A few comprehensive damage indexes are developed in the arithmetic and geometric mean of the higher statistical moments, and are classified by using the FCM clustering method to achieve nonlinear damage detection. A series of the measured response data, downloaded from the web site of the Los Alamos National Laboratory (LANL) USA, from a three-storey building structure considering the environmental variety as well as different nonlinear damage cases, are analyzed and used to assess the performance of the new nonlinear damage detection method. The effectiveness and robustness of the new proposed method are finally analyzed and concluded.

A Study on Visual Saliency Detection in Infrared Images Using Boolean Map Approach

  • Truong, Mai Thanh Nhat;Kim, Sanghoon
    • Journal of Information Processing Systems
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    • v.16 no.5
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    • pp.1183-1195
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    • 2020
  • Visual saliency detection is an essential task because it is an important part of various vision-based applications. There are many techniques for saliency detection in color images. However, the number of methods for saliency detection in infrared images is limited. In this paper, we introduce a simple approach for saliency detection in infrared images based on the thresholding technique. The input image is thresholded into several Boolean maps, and an initial saliency map is calculated as a weighted sum of the created Boolean maps. The initial map is further refined by using thresholding, morphology operation, and a Gaussian filter to produce the final, high-quality saliency map. The experiment showed that the proposed method has high performance when applied to real-life data.

Light Source Target Detection Algorithm for Vision-based UAV Recovery

  • Won, Dae-Yeon;Tahk, Min-Jea;Roh, Eun-Jung;Shin, Sung-Sik
    • International Journal of Aeronautical and Space Sciences
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    • v.9 no.2
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    • pp.114-120
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    • 2008
  • In the vision-based recovery phase, a terminal guidance for the blended-wing UAV requires visual information of high accuracy. This paper presents the light source target design and detection algorithm for vision-based UAV recovery. We propose a recovery target design with red and green LEDs. This frame provides the relative position between the target and the UAV. The target detection algorithm includes HSV-based segmentation, morphology, and blob processing. These techniques are employed to give efficient detection results in day and night net recovery operations. The performance of the proposed target design and detection algorithm are evaluated through ground-based experiments.