• IEIE Transactions on Smart Processing and Computing
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• 제4권2호
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• pp.110-114
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• 2015

This paper reports a novel object-detection technique in video sequences. The proposed algorithm consists of detection of objects in illuminated and occluded videos by using object features and a neural network technique. It consists of two functional modules: region-based object feature extraction and continuous detection of objects in video sequences with region features. This scheme is proposed as an enhancement of the Lowe's scale-invariant feature transform (SIFT) object detection method. This technique solved the high computation time problem of feature generation in the SIFT method. The improvement is achieved by region-based feature classification in the objects to be detected; optimal neural network-based feature reduction is presented in order to reduce the object region feature dataset with winner pixel estimation between the video frames of the video sequence. Simulation results show that the proposed scheme achieves better overall performance than other object detection techniques, and region-based feature detection is faster in comparison to other recent techniques.

• 대한임베디드공학회논문지
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• 제12권6호
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• pp.395-403
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• 2017

Image processing for object detection and identification has been studied for supply chain management application with various approaches. Among them, feature pointed detection algorithm is used to track an object or to recognize a position in automated supply chain systems and a depth image based feature point detection is recently highlighted in the application. The result of feature point detection is easily influenced by image noise. Also, the depth image has noise itself and it also affects to the accuracy of the detection results. In order to solve these problems, we propose a novel hybrid filtering mechanism for depth image based feature point detection, it shows better performance compared with conventional hybrid filtering mechanism.

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

• 제어로봇시스템학회논문지
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• 제19권12호
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• pp.1081-1088
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• 2013

Hyperspectral cameras acquire reflectance values at many different wavelength bands. Dimensions tend to increase because spectral information is stored in each pixel. Several attempts have been made to reduce dimensional problems such as the feature selection using Adaboost and dimension reduction using the Simulated Annealing technique. We propose a novel material detection method that consists of four steps: feature band selection, feature extraction, SVM (Support Vector Machine) learning, and target and specific region detection. It is a combination of the band ratio method and Simulated Annealing algorithm based on detection rate. The experimental results validate the effectiveness of the proposed feature selection and band ratio method.

• 융합신호처리학회논문지
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• 제16권2호
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• pp.44-49
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• 2015

텍스처가 부족한 장면이나 카메라 포즈 변화가 클 경우, 기존의 텍스처 기반의 특징 추적 방법의 신뢰도는 크게 떨어진다. LLAH와 같은 특징 사이의 기하 정보를 활용하는 토폴로지 기반 특징 기술 방법이 좋은 대안이 될 수 있으나, 특징 검출방법의 성능에 크게 영향을 받는다. 본 논문에서는 토폴로지 기반 특징 기술을 위한 효과적인 특징 검출 방법을 마련하기 위한 기초 연구로, OpenCV 라이브러리에서 제공되는 특징 검출 방법들의 반복성(repeatability) 분석을 통해 토폴로지 기반 특징 기술에의 적용 가능성을 살펴본다. 실험을 통해, FAST의 성능이 가장 우수함을 확인하였다.

• 인터넷정보학회논문지
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• 제7권3호
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• pp.143-153
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• 2006

얼굴 특징 검출은 HCI, 얼굴 인식, 얼굴 추적, 표정 인식 및 이미지 데이터 검색등과 같은 응용분야의 근간 기술이다. 실시간 환경에서 얼굴 특징 검출을 처리하기 위해서는 검출 알고리즘의 속도가 중요한 관건으로 작용하고 있다. 또한 빛의 변화, 대상의 위치, 각도, 복잡한 배경등과 같은 요인들은 얼굴 특징 검출 알고리즘의 검출율을 낮추는데 영향을 미치므로 이를 개선한 방법이 필요하다. 본 연구에서는 검출율과 검출 속도를 동시에 개선한 알고리즘을 제안한다. 제안 알고리즘은 얼굴 이미지에 빛 보상 알고리즘인 CLAHE를 이용하여 빛의 변화에 강건하도록 이미지를 개선한 다음 얼굴 피부 영역을 검출한다. 검출한 피부 영역에서 얼굴 특징 포인트를 추출하기 위해 얼굴 특징의 외형기반 기하학적 성질을 이용한다. 제안 알고리즘은 얼굴 특징 검출의 정확도를 높일 뿐 아니라 빠른 검출 속도를 보임으로써 얼굴 추적, 인식 등과 같은 실시간 응용분야에 적용할 수 있다.

• Journal of Power Electronics
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• 제16권2호
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• pp.760-768
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• 2016

Three-phase grid-connected inverters are widely applied in the fields of new energy power generation, electric vehicles and so on. Islanding detection is necessary to ensure the stability and safety of such systems. In this paper, feature recognition technology is applied and a novel islanding detection method is proposed. It can identify the features of inverter systems. The theoretical values of these features are defined as codebooks. The difference between the actual value of a feature and the codebook is defined as the quantizing distortion. When islanding happens, the sum of the quantizing distortions exceeds the threshold value. Thus, islanding can be detected. The non-detection zone can be avoided by choosing reasonable features. To accelerate the speed of detection and to avoid miscalculation, an active islanding detection method based on feature recognition technology is given. Compared to the active frequency or phase drift methods, the proposed active method can reduce the distortion of grid-current when the inverter works normally. The principles of the islanding detection method based on the feature recognition technology and the improved active method are both analyzed in detail. An 18 kVA DSP-based three-phase inverter with the SVPWM control strategy has been established and tested. Simulation and experimental results verify the theoretical analysis.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.5132-5148
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• 2017

Cyber attacks are evolving commensurate with recent developments in information security technology. Intrusion detection systems collect various types of data from computers and networks to detect security threats and analyze the attack information. The large amount of data examined make the large number of computations and low detection rates problematic. Feature selection is expected to improve the classification performance and provide faster and more cost-effective results. Despite the various feature selection studies conducted for intrusion detection systems, it is difficult to automate feature selection because it is based on the knowledge of security experts. This paper proposes a feature selection technique to overcome the performance problems of intrusion detection systems. Focusing on feature selection, the first phase of the proposed system aims at constructing a feature subset using a sequential forward floating search (SFFS) to downsize the dimension of the variables. The second phase constructs a classification model with the selected feature subset using a random forest classifier (RFC) and evaluates the classification accuracy. Experiments were conducted with the NSL-KDD dataset using SFFS-RF, and the results indicated that feature selection techniques are a necessary preprocessing step to improve the overall system performance in systems that handle large datasets. They also verified that SFFS-RF could be used for data classification. In conclusion, SFFS-RF could be the key to improving the classification model performance in machine learning.

• 정보보호학회논문지
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• 제33권6호
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• pp.881-891
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• 2023

최근에는 인공지능을 활용하여 악성 URL을 탐지하는 다양한 연구가 진행되고 있으며, 대부분의 연구 결과에서 높은 탐지 성능을 보였다. 그러나 고전 머신러닝을 활용하는 경우 feature를 분석하고 선별해야 하는 추가 비용이 발생하며, 데이터 분석가의 역량에 따라 탐지 성능이 결정되는 이슈가 있다. 본 논문에서는 이러한 이슈를 해결하기 위해 URL lexical feature를 자동으로 추출하는 딥러닝 모델의 일부가 고전 머신러닝 모델에 결합된 형태인 DL-ML Fusion Hybrid 모델을 제안한다. 제안한 모델로 직접 수집한 총 6만 개의 악성과 정상 URL을 학습한 결과 탐지 성능이 최대 23.98%p 향상되었을 뿐만 아니라, 자동화된 feature engineering을 통해 효율적인 기계학습이 가능하였다.