• 전기전자학회논문지
• /
• 제20권1호
• /
• pp.82-88
• /
• 2016

영상 인식에서 널리 이용되는 PPHT(Progressive Probability Hough Transform)는 직선을 정확하게 인식하는 우수한 알고리즘이나 원본 영상이 선명하지 않거나 복잡하여 잡음 성분이 많은 경우 인식률이 감소하는 문제가 있다. 이러한 문제를 해결하기 위해 잡음에 강하고 손상된 가장자리 패턴을 복구하며 직선을 인식하는 개선된 PPHT 방식을 제안한다. 제안하는 알고리즘은 픽셀 단위로 직선을 추적하고 검증하여 선분을 검출하는 방식으로 잡음의 영향을 최소화하고 손상된 가장자리 패턴을 일정 범위 내에서 복구하여 인식률을 증가시켰다. 제안한 알고리즘을 차선 인식에 적용하여 직선의 오인식률을 30% 이상 감소시키고 선분 인식률이 15%까지 증가함을 확인하였다.

• Smart Structures and Systems
• /
• 제14권2호
• /
• pp.85-104
• /
• 2014

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권7호
• /
• pp.2131-2153
• /
• 2022

To achieve accurate detection of tuberculosis (TB) areas in chest radiographs, we design a chest X-ray TB area detection algorithm. The algorithm consists of two stages: the chest X-ray TB classification network (CXTCNet) and the chest X-ray TB area detection network (CXTDNet). CXTCNet is used to judge the presence or absence of TB areas in chest X-ray images, thereby excluding the influence of other lung diseases on the detection of TB areas. It can reduce false positives in the detection network and improve the accuracy of detection results. In CXTCNet, we propose a channel attention mechanism (CAM) module and combine it with DenseNet. This module enables the network to learn more spatial and channel features information about chest X-ray images, thereby improving network performance. CXTDNet is a design based on a sparse object detection algorithm (Sparse R-CNN). A group of fixed learnable proposal boxes and learnable proposal features are using for classification and location. The predictions of the algorithm are output directly without non-maximal suppression post-processing. Furthermore, we use CLAHE to reduce image noise and improve image quality for data preprocessing. Experiments on dataset TBX11K show that the accuracy of the proposed CXTCNet is up to 99.10%, which is better than most current TB classification algorithms. Finally, our proposed chest X-ray TB detection algorithm could achieve AP of 45.35% and AP50 of 74.20%. We also establish a chest X-ray TB dataset with 304 sheets. And experiments on this dataset showed that the accuracy of the diagnosis was comparable to that of radiologists. We hope that our proposed algorithm and established dataset will advance the field of TB detection.

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송공학회 2009년도 IWAIT
• /
• pp.229-232
• /
• 2009

Shot change detection is an important technique for effective management of video data, so detection scheme requires adaptive detection techniques to be used actually in various video. In this paper, we propose an adaptive shot change detection algorithm using the mean of feature value on variable reference blocks. Our algorithm determines shot change detection by defining adaptive threshold values with the feature value extracted from video frames and comparing the feature value and the threshold value. We obtained better detection ratio than the conventional methods maximally by 15% in the experiment with the same test sequence. We also had good detection ratio for other several methods of feature extraction and could see real-time operation of shot change detection in the hardware platform with low performance was possible by implementing it in TVUS model of HOMECAST Company. Thus, our algorithm in the paper can be useful in PMP or other portable players.

• 정보보호학회논문지
• /
• 제16권6호
• /
• pp.111-121
• /
• 2006

인터넷이 빠르게 성장함에 따라 네트워크 공격기법이 변화되고 새로운 공격 형태가 나타나고 있다. 네트워크상에서 알려진 침입의 탐지는 효율적으로 수행되고 있으나 알려지지 않은 침입에 대해서는 오탐지(false negative)나 과탐지(false positive)가 너무 높게 나타난다. 또한, 네트워크상에서 지속적으로 처리되는 대량의 패킷에 대하여 실시간적인 탐지와 새로운 침입 유형에 대한 대응방법과 인지능력에 한계가 있다. 따라서 다양한 대량의 트래픽에 대해서 탐지율을 높이고 과탐지를 감소할 수 있는 방법이 필요하다. 본 논문에서는 네트워크 기반의 이상 침입 탐지 시스템에서 과탐지를 감소하고, 침입 탐지 능력을 향상시키기 위하여 다차원 연관 규칙 마이닝과 수정된 부정 선택 알고리즘(Negative Selection Algorithm)을 결합한 다중 레벨 이상 침입 탐지 기술을 제안한다. 제안한 알고리즘의 성능 평가를 위하여 기존의 이상 탐지 알고리즘과 제안된 알고리즘을 수행하여, 각각의 과탐지율을 평가, 제시하였다.

• 대한음성학회:학술대회논문집
• /
• 대한음성학회 2003년도 5월 학술대회지
• /
• pp.137-140
• /
• 2003

In this paper, we propose a robust endpoint detection algorithm for speaker verification. Proposed algorithm uses energy and cepstral distance parameters, and it replaces the detected endpoints with endpoints of voiced speech, when the estimated signal-to-noise ratio (SNR) is low. Experimental results show that proposed algorithm is superior to energy-based endpoint detection algorithm.

• 말소리와 음성과학
• /
• 제4권3호
• /
• pp.103-110
• /
• 2012

In this paper, a target signal detection method using multiple signal classification (MUSIC) algorithm is proposed. The MUSIC algorithm is a subspace-based direction of arrival (DOA) estimation method. Using the inverse of the eigenvalue-weighted eigen spectra, the algorithm detects the DOAs of multiple sources. To apply the algorithm in target signal detection for GSC-based beamforming, we utilize its spectral response for the DOA of the target source in noisy conditions. The performance of the proposed target signal detection method is compared with those of the normalized cross-correlation (NCC), the fixed beamforming, and the power ratio method. Experimental results show that the proposed algorithm significantly outperforms the conventional ones in receiver operating characteristics (ROC) curves.

• Current Optics and Photonics
• /
• 제2권1호
• /
• pp.69-78
• /
• 2018

This paper presents a precise edge detection algorithm for the critical dimension (CD) measurement of a Thin-Film Transistor Liquid-Crystal Display (TFT-LCD) pattern. The sigmoid surface function is proposed to model the blurred step edge. This model can simultaneously find the position and geometry of the edge precisely. The nonlinear least squares fitting method (Levenberg-Marquardt method) is used to model the image intensity distribution into the proposed sigmoid blurred edge model. The suggested algorithm is verified by comparing the CD measurement repeatability from high-magnified blurry and noisy TFT-LCD images with those from the previous Laplacian of Gaussian (LoG) based sub-pixel edge detection algorithm and error function fitting method. The proposed fitting-based edge detection algorithm produces more precise results than the previous method. The suggested algorithm can be applied to in-line precision CD measurement for high-resolution display devices.

• 한국방재학회:학술대회논문집
• /
• 한국방재학회 2011년도 정기 학술발표대회
• /
• pp.39-39
• /
• 2011

This study presents an effective vision-based system for cable bridge damage detection. In theory, cable bridges need to be inspected the outer as well as the inner part. Starting from August 2010, a new research project supported by Korea Ministry of Land, Transportation Maritime Affairs(MLTM) was initiated focusing on the damage detection of cable system. In this study, only the surface damage detection algorithm based on a vision-based system will be focused on, an overview of the vision-based cable damage detection is given in Fig. 1. Basically, the algorithm combines the image enhancement technique with principal component analysis(PCA) to detect damage on cable surfaces. In more detail, the input image from a camera is processed with image enhancement technique to improve image quality, and then it is projected into PCA sub-space. Finally, the Mahalanobis square distance is used for pattern recognition. The algorithm was verified through laboratory tests on three types of cable surface. The algorithm gave very good results, and the next step of this study is to implement the algorithm for real cable bridges.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2006년도 하계종합학술대회
• /
• pp.305-306
• /
• 2006

To avoid the large scale of damage when fire occurs in the tunnel, it is necessary to have a system to minimize the damage, and early discovery of the problem. In this paper, we have proposed algorithm using the image processing, which is the high-speed detection for the occurrence of fire or smoke in the tunnel. The fire detection is different to the forest fire detection as there are elements such as car and tunnel lightings and other variety of elements different from the forest environment. Therefore, an indigenous algorithm should be developed.The two algorithms proposed in this paper, are able to complement with each other and also they can detect the exact position, at the earlier stay of detection. In addition, by comparing properties of each algorithm throughout this experiment, we have proved the propriety of algorithm.