• 반도체디스플레이기술학회지
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• 제12권4호
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• pp.33-38
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• 2013

Image retrieval is one of the most exciting and fastest growing research fields in the area of multimedia technology. As the amount of digital contents continues to grow users are experiencing increasing difficulty in finding specific images in their image libraries. This paper proposes an efficient image descriptor which uses a local color and texture in the non-overlapped block images. To evaluate the performance of the proposed method, we assessed the retrieval efficiency in terms of ANMRR with common image dataset. The experimental trials revealed that the proposed algorithm exhibited a significant improvement in ANMRR, compared to Dominant Color Descriptor and Edge Histogram Descriptor.

• 한국멀티미디어학회논문지
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• 제17권12호
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• pp.1418-1429
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• 2014

In this paper we suggest a new two-wheelers detection algorithm using local cell features. The first, we propose new feature vector matrix extraction algorithm using the correlation two cells based on local cell histogram and shifting from the result of histogram of oriented gradients(HOG). The second, we applied new weighting values which are calculated by the modified histogram intersection showing the similarity of two cells. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2010년 춘계학술발표회 논문집
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• pp.221-224
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• 2010

When the image registration methods which were generally used to the low medium resolution satellite images is applied to the high spatial resolution images, some matching errors or limitations might be occurred because of the local distortions in the images. This paper, therefore, proposed the automatic image-to-image registration of high resolution satellite images using local properties of control points to improve the registration result.

• 한국산업정보학회논문지
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• 제22권6호
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• pp.39-45
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• 2017

기존의 색상기반 얼굴인식 방법은 조명변화에 민감하며, 위변조의 가능성이 있기 때문에 다양한 산업분야에 적용되기 어려운 문제가 있었다. 본 논문에서는 이러한 문제를 해결하기 위해 깊이 영상을 이용한 지역 이진 패턴(LBP) 기반의 얼굴인식 방법을 제안한다. 깊이 정보를 이용한 얼굴 검출 방법과 얼굴 인식을 위한 특징 추출 및 매칭 방법을 구현하고, 모의실험 결과를 바탕으로 제안된 방식의 인식 성능을 나타낸다.

• 설비공학논문집
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• 제29권7호
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• pp.366-372
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• 2017

The objective of this study was to characterize Waveguide-Blow-Cutoff (WBC) array for Electromagnetic Pulse (EMP) shield in air duct or water pipe, the typical pathway of pulse in indoor space with critical electronic device. A numerical investigation with three different WBC designs (circular, rectangular, and hexagonal or honeycomb) was conducted to satisfy recommended shielding effectiveness (SE) levels from 80 dB to 140 dB. Pressure drop between upstream and downstream of EMP shields based on WBC arrays was also investigated to understand air flow feature in air duct of HVAC system. Results showed that honeycomb geometry outperformed other shapes in terms of reducing the depth of EMP shield, thus providing better air flow in duct path with lower local loss coefficient in HVAC system under SE requirements.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2013년도 추계학술대회
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• pp.8-11
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• 2013

We implement the foveation and frequency sensitivity feature of human visual system in discrete cosine transform (DCT) domain. Resolution of human visual perception decays as distance from the eye-focused point, known as foveation property, and the middle frequency components give most pleasant image quality to human than the low and high frequency components, which is the frequency sensitivity property of human visual system. For satisfying the foveation property, we enhanced the local contrast at the focused regions and smoothed local contrast at the non-focused regions in the DCT domain without bringing the blocking and ringing artifacts. Moreover, the energies at each DCT frequency components is modified with various degree to fulfill the frequency sensitivity property. The proposed method is verified by the subjective and objective evaluations that it can the improve the human perceptual visual quality.

• 한국산학기술학회논문지
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• 제8권5호
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• pp.1098-1103
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• 2007

본 논문에서는 칼라 영상에서 적목(red eye)의 자동 검출 및 제거 방법을 제안한다. 제안한 방법은 적색도(redness)와 기하학적 특징에 기반하여 초기 적목 영역을 검출하고, 초기 적목 영역 주위의 국소적 특성을 반영하여 최종 적목 영역을 검출한다. 최종 적목 영역에 대해 소프트 제거에 기반한 방법을 사용하여 적목을 제거한다. 실험에서 제안한 방법은 Willamowski와 Csurka[1]의 방법에 비해 적목 영역의 검출과 제거 결과가 개선되었다.

• 한국멀티미디어학회논문지
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• 제19권9호
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• pp.1659-1668
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• 2016

Understanding 3D structure of scenes is of a great interest in various vision-related tasks. In this paper, we present a unified approach for estimating depth from a single monocular image. The key idea of our approach is to take advantages both of parametric learning and non-parametric sampling method. Using a parametric convolutional network, our approach learns the relation of various monocular cues, which make a coarse global prediction. We also leverage the local prediction to refine the global prediction. It is practically estimated in a non-parametric framework. The integration of local and global predictions is accomplished by concatenating the feature maps of the global prediction with those from local ones. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively.

• 반도체디스플레이기술학회지
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• 제8권3호
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• pp.39-44
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• 2009

Local Binary Pattern (LBP) is becoming a popular tool for various machine vision applications such as face recognition, classification and background subtraction. In this paper, we propose a new extension of LBP, called the Diagonal LBP (DLBP), to handle the image-based gender classification problem arise in interactive display systems. Instead of comparing neighbor pixels with the center pixel, DLBP generates codes by comparing a neighbor pixel with the diagonal pixel (the neighbor pixel in the opposite side). It can reduce by half the code length of LBP and consequently, can improve the computation complexity. The Support Vector Machine is utilized as the gender classifier, and the texture profile based on DLBP is adopted as the feature vector. Experimental results revealed that our approach based on the diagonal LPB is very efficient and can be utilized in various real-time pattern classification applications.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.242-252
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• 2021

Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance's importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.