• 말소리와 음성과학
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• 제1권4호
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• pp.111-117
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• 2009

In this paper, a new speech endpoint detector in noisy environment is proposed. According to the previous research, the energy feature in the speech region is easily distinguished from that in the speech absent region. In conventional method, the endpoint can be found by applying the edge detection filter that finds the abrupt changing point in feature domain. However, since the frame energy feature is unstable in noisy environment, the accurate edge detection is not possible. Therefore, in this paper, the novel feature extraction method based on spectrum envelop pattern is proposed. Then, the edge detection filter is applied to the proposed feature for detection of the endpoint. The experiments are performed in the car noise environment and a substantial improvement was obtained over the conventional method.

• 대한의용생체공학회:의공학회지
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• 제18권4호
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• pp.357-364
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• 1997

유방암은 그 조기 발견이 암환자의 사망률을 줄이는 데 있어서 가장 중요한 요소임을 알려져 있다. 스크리닝 검사에 의해 발견되는 유방암의 20%정도를 차지하는 DCIS(ductal carcinoma in situ)의 경우 미세석회화만이 필름 상에서 볼 수 있는 유일한 소견이다. 따라서 미세석회화를 발견하고 그 형태와 분포의 분석을 통한 진단이 암의 조기 발견에 매우 중요하다. 이 검출과정을 자동화하려는 시도가 디지털 영상처리 기술의 관심이 되어 왔다. 본 연구에서는 상관계수를 특징(feature)으로 사용하여 성능을 향상시킨 통계적 패턴 분류법을 제안하였다. 결과적인 검출율은 통계적 문턱치 설정에 의한 이진호 방법과 비교하여 48%에서 83%로 향상되었다. 성능은 TP와 FP로 평가되었으며 클래스 구분시의 오차도 함께 나타내었다.

• 한국정보기술학회논문지
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• 제16권12호
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• pp.75-83
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• 2018

최근 자율 주행 자동차에 대한 연구가 활발하다. 자율 주행 자동차는 보행자 검출 및 인식 기술이 중요하다. 최근에 주로 사용되는 CNN(Convolutional Neural Network)을 이용한 보행자 검출은 대체로 좋은 성능을 보이나 영상의 환경에 따른 성능 저하가 있다. 본 논문에서는 LGP-FL(Local Gradient Pattern-Feature Layer)을 추가한 CNN Network를 기반으로 해마 신경망의 장기 기억 구조를 적용한 보행자 검출 시스템을 제안한다. 먼저 입력 이미지를 $227{\times}227$의 크기로 변경한다. 그 후 총 5개 층의 Convolution layer를 거쳐 특징을 추출한다. 그 과정에서 추가되는 LGP-FL에서는 LGP 특징 패턴을 추출하여 출현 빈도수가 높은 패턴을 장기 기억 장치에 저장한다. 이후 검출 과정에서 밝기 및 색상 변화에 강인한 LGP 특징 패턴 정보를 이용해 검출함으로써 보다 정확하게 보행자를 검출할 수 있다. 기존의 방법들과 제안하는 기법의 비교를 통해 약 1~4%의 검출률 증가를 확인하였다.

• 센서학회지
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• 제20권6호
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• pp.388-392
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• 2011

In this paper, a face detection method for interactive TV control system using a new feature, edge histogram feature, with a support vector machine(SVM) in the near-infrared(NIR) images is proposed. The edge histogram feature is extracted using 16-directional edge intensity and a histogram. Compared to the previous method using local binary pattern(LBP) feature, the proposed method using edge histogram feature has better performance in both smaller feature size and lower equal error rate(EER) for face detection experiments in NIR databases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.5023-5038
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• 2017

Big data is an emerging technology which deals with wide range of data sets with sizes beyond the ability to work with software tools which is commonly used for processing of data. When we consider a huge network, we have to process a large amount of network information generated, which consists of both normal and abnormal activity logs in large volume of multi-dimensional data. Intrusion Detection System (IDS) is required to monitor the network and to detect the malicious nodes and activities in the network. Massive amount of data makes it difficult to detect threats and attacks. Sequential Pattern mining may be used to identify the patterns of malicious activities which have been an emerging popular trend due to the consideration of quantities, profits and time orders of item. Here we propose a sequential pattern mining algorithm with fuzzy logic feature selection and fuzzy weighted support for huge volumes of network logs to be implemented in Apache Hadoop YARN, which solves the problem of speed and time constraints. Fuzzy logic feature selection selects important features from the feature set. Fuzzy weighted supports provide weights to the inputs and avoid multiple scans. In our simulation we use the attack log from NS-2 MANET environment and compare the proposed algorithm with the state-of-the-art sequential Pattern Mining algorithm, SPADE and Support Vector Machine with Hadoop environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1989-2011
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• 2019

Vehicle detection based on aerial images is an interesting and challenging research topic. Most of the traditional vehicle detection methods are based on the sliding window search algorithm, but these methods are not sufficient for the extraction of object features, and accompanied with heavy computational costs. Recent studies have shown that convolutional neural network algorithm has made a significant progress in computer vision, especially Faster R-CNN. However, this algorithm mainly detects objects in natural scenes, it is not suitable for detecting small object in aerial view. In this paper, an accurate and effective vehicle detection algorithm based on Faster R-CNN is proposed. Our method fuse a hyperactive feature map network with Eltwise model and Concat model, which is more conducive to the extraction of small object features. Moreover, setting suitable anchor boxes based on the size of the object is used in our model, which also effectively improves the performance of the detection. We evaluate the detection performance of our method on the Munich dataset and our collected dataset, with improvements in accuracy and effectivity compared with other methods. Our model achieves 82.2% in recall rate and 90.2% accuracy rate on Munich dataset, which has increased by 2.5 and 1.3 percentage points respectively over the state-of-the-art methods.

• 한국멀티미디어학회논문지
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• 제18권10호
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• pp.1197-1204
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• 2015

Smoke detection plays an important role for the early detection of fire. In this paper, we suggest a newly developed method that generated LBPV(Local Binary Pattern Variance)s as special feature vectors from RGB contrast images can be applied to detect smoke using SVM(Support Vector Machine). The proposed method rearranges mean value of the block from each R, G, B channel and its intensity of the mean value. Additionally, it generates RGB contrast image which indicates each RGB channel’s contrast via smoke’s achromatic color. Uniform LBPV, Rotation-Invariance LBPV, Rotation-Invariance Uniform LBPV are applied to RGB Contrast images so that it could generate feature vector from the form of LBP. It helps to distinguish between smoke and non smoke area through SVM. Experimental results show that true positive detection rate is similar but false positive detection rate has been improved, although the proposed method reduced numbers of feature vector in half comparing with the existing method with LBP and LBPV.

• Journal of the Korean Data and Information Science Society
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• 제18권2호
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• pp.315-326
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• 2007

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents a dynamic anomaly detection scheme that can effectively identify a group of especially harmful internal masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on the feature values, the use pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function with both the age of the user profile and weighted feature values. The performance of our scheme is evaluated by a simulation. Simulation results demonstrate that the anomalies are well detected by the proposed dynamic scheme that considers the age of user profiles.

• 한국의류산업학회지
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• 제12권5호
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• pp.582-590
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• 2010

Garment design needs an iterative manipulation of 2D patterns to generate a final sloper. Traditionally there have been two kinds of design methodologies such as the flat pattern method and the pattern draping method. But today, it is possible to combine the advantages from the two methods due to the realistic cloth simulation techniques. We devised a new garment design system which starts from 3D initial drape simulation result and then modifies the garment by editing the 2D flat patterns synchronously. With this interactive methodology using real-time pattern deformation technique, the designer can freely change a pattern shape by watching its 3D outlook in real-time. Also the final garment data were given relative coordinates with respect to the human anthropometric feature points detected by an automatic body feature detection algorithm. Using the relative human body coordinate system, the final garments can be re-used to an arbitrary body data without repositioning in the drape simulation. A female shirt was used for an example and a 3D body scan data was used for an illustration of the feature point detection algorithm.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제7권9호
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• pp.345-350
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• 2018

본 연구는 지정맥 인식에 중요한 정맥 패턴 특징검출을 위한 알고리즘이다. 특징검출 알고리즘은 패턴인식 시 인식결과에 많은 영향을 끼치므로 중요하다. 인식률은 손가락 위치 변화에 따라 기준도 변화되므로 저하되는 특징을 가지고 있다. 또한, 손가락에 적외선 광을 조사하여 획득한 영상은 영상 배경과 혈관 패턴을 분리하기에 어렵고, 영상 전처리과정을 수행하므로 검출시간이 증대되는 특징을 가지고 있다. 이를 위해, 제시하는 알고리즘은 영상 전처리과정이 없이 수행되어 검출 시간을 줄일 수 있고, 지정맥 영상에 SWDA(Shifted Waveform Data Analysis) 알고리즘을 적용하여 손가락 마디 위치 및 정맥 패턴 검출이 가능한 특징을 가지고 있다. 적외선 투과율이 낮아 상대적으로 어두운 정맥 영상도 검출 오류 최소화가 가능한 특징을 보였다. 또한, 손가락 마디 위치는 분류 단계에서 기준으로 활용하면 인식률 저하를 보완할 수 있는 특징을 가지고 있다. 추후 손바닥, 손목 등 신체 여러 인식분야에 제안하는 알고리즘을 적용한다면 생체 특징 검출 정확도 향상 및 인식 수행 시간 감소에 기여할 것으로 기대된다.