• 감성과학
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• 제7권2호
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• pp.133-140
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• 2004

최근 대량으로 쏟아지는 감성공학 연구 결과와 논문들을 가치 있는 자료로 만들기 위해서는 감성 데이터가 산업 전반에 활용될 수 있도록 지표로 정리해야 한다. 본 논문에서는 "웹기반 감성 데이터 베이스 구축 및 보급에 관한 연구" 과제를 통해 작성된 감성 데이터 지표에 입각해서 앞으로 대량으로 출현할 감성공학 데이터의 지속적인 지표화를 위한 과정의 자동화를 제안한다. 문서 데이터의 지표화 작업이 자동요약과 유사하다는 점에 착안하여 자동지표화 시스템을 위한 기술들의 기초가 되는 정보유형 및 주요어 추출, 특성표현을 통한 정보문 추출에 대해 감성공학 코퍼스 분석을 통해 연구하고자 한다. 이는 감성공학 분야에서의 지식관리 시스템이나 자동요약 시스템에 활용될 수 있다. 활용될 수 있다.

• International Journal of Automotive Technology
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• 제6권2호
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• pp.171-181
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• 2005

The purpose of this paper is two-fold: 1) A novel algorithm in order to extract lane-related information from road images is presented; 2) Design specifications of an image processing onboard unit capable of extracting lane­related information in real-time is also presented. Obtaining precise information from road images requires many features due to the effects of noise that eventually leads to long processing time. By exploiting a FPGA and DSP, we solve the problem of real-time processing. Due to the fact that image processing of road images relies largely on edge features, the FPGA is adopted in the hardware design. The schematic configuration of the FPGA is optimized in order to perform 3 $\times$ 3 Sobel edge extraction. The DSP carries out high-level image processing of recognition, decision, estimation, etc. The proposed algorithm uses edge features to define an Edge Distribution Function (EDF), which is a histogram of edge magnitude with respect to the edge orientation angle. The EDF enables the edge-related information and lane-related to be connected. The performance of the proposed system is verified through the extraction of lane-related information. The experimental results show the robustness of the proposed algorithm and a processing speed of more than 25 frames per second, which is considered quite successful.

• 대한임베디드공학회논문지
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• 제17권6호
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• pp.309-317
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• 2022

In this paper, we propose a machine learning-based copy-move forgery detection network with dual branches. Because the rotation or scaling operation is frequently involved in copy-move forger, the conventional convolutional neural network is not effectively applied in detecting copy-move tampering. Therefore, we divide the input into rotation-invariant and scaling-invariant features based on the wavelet coefficients. Each of the features is input to different branches having the same structure, and is fused in the combination module. Each branch comprises feature extraction, correlation, and mask decoder modules. In the proposed network, VGG16 is used for the feature extraction module. To check similarity of features generated by the feature extraction module, the conventional correlation module used. Finally, the mask decoder model is applied to develop a pixel-level localization map. We perform experiments on test dataset and compare the proposed method with state-of-the-art tampering localization methods. The results demonstrate that the proposed scheme outperforms the existing approaches.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.325-327
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• 2004

ECG limb lead II signal widely used to diagnosis heart diseases and it is essential to detect ECG events (onsets, offsets and peaks of the QRS complex P wave and T wave) and extract them from ECG signal for heart diseases diagnoses. However, it is very difficult to develop standardized feature extraction formulas since ECG signals are varying on patients and disease types. In this paper, simple feature extraction method from normal and abnormal types of ECG signals is proposed. As a signal features, heart rate, PR interval, QRS interval, QT interval, interval between S wave and baseline, and T wave types are extracted. To show the validity of proposed method, Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Sinus Bradycardia, and Sinus Tachycardia data from MIT-BIH arrhythmia database are used for feature extraction and the extraction results showed higher extraction capability compare to conventional formula based extraction method.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.344-354
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• 2015

In order to study the development of partial discharge (PD) under typical protrusion defects in gas-insulated switchgear, we applied step voltages on the defect and obtained the ${\varphi}-u$ and ${\varphi}-n$ spectrograms of ultra-high frequency (UHF) PD signals in various PD stages. Furthermore, we extracted seven kinds of features to characterize the degree of deterioration of insulation and analyzed their values, variation trends, and change rates. These characteristics were inconsistent with the development of PD. Hence, the differences of these features could describe the severity of PD. In addition, these characteristics could provide integrated characteristics regarding PD development and improve the reliability of PD severity assessment because these characteristics were extracted from different angles. To explain the variation laws of these seven kinds of parameters, we analyzed the relevant physical mechanism by considering the microphysical process of PD formation and development as well as the distortion effect generated by the space charges on the initial field. The relevant physical mechanism effectively allocated PD severity among these features for assessment, and the effectiveness and reliability of using these features to assess PD severity were proved by testing a large number of PD samples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1595-1613
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• 2017

The IR camera and laser-based IR projector provide an effective solution for real-time collection of moving targets in RGB-D videos. Different from the traditional RGB videos, the captured depth videos are not affected by the illumination variation. In this paper, we propose a novel feature extraction framework to describe human activities based on the above optical video capturing method, namely spatial-temporal texture features for 3D human activity recognition. Spatial-temporal texture feature with depth information is insensitive to illumination and occlusions, and efficient for fine-motion description. The framework of our proposed algorithm begins with video acquisition based on laser projection, video preprocessing with visual background extraction and obtains spatial-temporal key images. Then, the texture features encoded from key images are used to generate discriminative features for human activity information. The experimental results based on the different databases and practical scenarios demonstrate the effectiveness of our proposed algorithm for the large-scale data sets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권5호
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• pp.1856-1869
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• 2015

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.

• 한국콘텐츠학회논문지
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• 제6권5호
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• pp.20-28
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• 2006

본 논문은 공간주파수 특징들과 다중 해상도 특징들을 가진 웨이블렛 영역에서 추출된 각 대역의 시각 특징 추출과 이들의 퍼지 적분 조합에 대하여 제안하였다. 칼라 양자화 이후에 똑같은 칼라의 빈도를 취함으로써 기존의 칼라 히스토그램 인터섹션 방법의 단점인 양자화 에러를 줄일 수 있게 칼라 특징을 표현한다. 또한 유사도는 서로 독립적인 특성을 갖는 호모그램, 칼라, 에너지 특징을 퍼지 측도와 퍼지 적분을 사용하여 조합한다. 1,000개의 칼라 영상에 대하여 실험을 하였고, 제안된 방법이 기존 방법들보다 객관적이고 주관적인 성능에서 우수함을 보였다.

• 한국인터넷방송통신학회논문지
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• 제13권2호
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• pp.67-75
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• 2013

우리나라는 삼면이 바다인 환경적 특성으로 인해 연안 양식 산업이 발전해 왔다. 번식력과 포식성이 매우 강한 아무르불가사리에 의해 우리나라 근해의 양식 산업이 받는 피해가 매년 급증하고 있다. 더욱이 아무르불가사리는 살아있는 어패류를 잡아먹기 때문에 양식 어민들의 피해가 매우 크다. 이 논문에서는 수중에서 획득한 아무르불가사리 영상에서 효과적으로 특징을 추출하는 방법을 제안하였다. 제안한 방법에서는 6-방향 스캐닝을 이용한 볼록 특징 추출을 사용하여 기존 방법에 비해서 적은 수의 특징 후보를 산출한다. 또한, 추출된 볼록 특징과 영역 중심점을 이용하여 후보 오목점을 선정한 후 최종 오목 특징을 추출한다. 군집 생활을 하는 불가사리의 특성 때문에 입력 영상의 불가사리 개체도 밀집되어 있다. 따라서 입력 영상에서 추출되는 특징 후보의 수를 최소화하는 것은 중요한 의미를 갖는다. 실험결과, 제안한 특징 추출 방법은 특징 후보수 대비 특징 추출률이 약 88%로 기존 방법에 비해 개선되었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.310-315
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• 1999

We extracted narrow open water streams from JERS-1 SAR images of the Amazon rain forest. The extracted range of these streams were almost comparable to a high level extraction of the same streams from near-IR images of JERS-1 VNIR data notwithstanding that these features in SAR images show the strong dependence of the observation angle. Large water bodies are relatively easy to extract from JERS-1 SAR images, as they tend to appear as very dark areas; but streams whose width is nearly equal to or less than the spatial resolution no longer appear as very dark features. By using strong scatterers distributed sparsely along the radar facing sides of the streams, we can successfully estimate approximate ranges of waterways and then extract relatively dark line-like features within these ranges.