• 제목/요약/키워드: Feature space

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모바일 디바이스를 이용한 3차원 특징점 추출 기법 (3D feature point extraction technique using a mobile device)

  • 김진겸;서영호
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2022년도 추계학술대회
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    • pp.256-257
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    • 2022
  • 본 논문에서는 단일 모바일 디바이스의 움직임을 통해 3차원 특징점을 추출하는 방법에 대해 소개한다. 단안 카메라를 이용해 카메라 움직임에 따라 2D 영상을 획득하고 Baseline을 추정한다. 특징점 기반의 스테레오 매칭을 진행한다. 특징점과 디스크립터를 획득하고 특징점을 매칭한다. 매칭된 특징점을 이용해 디스패리티를 계산하고 깊이값을 생성한다. 3차원 특징점은 카메라 움직임에 따라 업데이트 된다. 마지막으로 장면 전환 검출을 이용하여 장면 전환시 특징점을 리셋한다. 위 과정을 통해 특징점 데이터베이스에 평균 73.5%의 저장공간 추가 확보를 할 수 있다. TUM Dataset의 Depth Ground truth 값과 RGB 영상으로 제안한 알고리즘을 적용하여 3차원 특징점 결과와 비교하여 평균 26.88mm의 거리 차이가 나는것을 확인하였다.

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Eigen - Environment 잡음 보상 방법을 이용한 강인한 음성인식 (Robust Speech Recognition using Noise Compensation Method Based on Eigen - Environment)

  • 송화전;김형순
    • 대한음성학회지:말소리
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    • 제52호
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    • pp.145-160
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    • 2004
  • In this paper, a new noise compensation method based on the eigenvoice framework in feature space is proposed to reduce the mismatch between training and testing environments. The difference between clean and noisy environments is represented by the linear combination of K eigenvectors that represent the variation among environments. In the proposed method, the performance improvement of speech recognition systems is largely affected by how to construct the noisy models and the bias vector set. In this paper, two methods, the one based on MAP adaptation method and the other using stereo DB, are proposed to construct the noisy models. In experiments using Aurora 2 DB, we obtained 44.86% relative improvement with eigen-environment method in comparison with baseline system. Especially, in clean condition training mode, our proposed method yielded 66.74% relative improvement, which is better performance than several methods previously proposed in Aurora project.

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PPD: A Robust Low-computation Local Descriptor for Mobile Image Retrieval

  • Liu, Congxin;Yang, Jie;Feng, Deying
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제4권3호
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    • pp.305-323
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    • 2010
  • This paper proposes an efficient and yet powerful local descriptor called phase-space partition based descriptor (PPD). This descriptor is designed for the mobile image matching and retrieval. PPD, which is inspired from SIFT, also encodes the salient aspects of the image gradient in the neighborhood around an interest point. However, without employing SIFT's smoothed gradient orientation histogram, we apply the region based gradient statistics in phase space to the construction of a feature representation, which allows to reduce much computation requirements. The feature matching experiments demonstrate that PPD achieves favorable performance close to that of SIFT and faster building and matching. We also present results showing that the use of PPD descriptors in a mobile image retrieval application results in a comparable performance to SIFT.

라플라스 스케일스페이스 이론과 적응 문턱치를 이용한 크기 불변 표적 탐지 기법 (Scale Invariant Target Detection using the Laplacian Scale-Space with Adaptive Threshold)

  • 김성호;양유경
    • 한국군사과학기술학회지
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    • 제11권1호
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    • pp.66-74
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    • 2008
  • This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection. Scale invariant feature using the Laplacian scale-space can detect different sizes of targets robustly compared to the conventional spatial filtering methods with fixed kernel size. Additionally, scale-reflected adaptive thresholding can reduce many false alarms. Experimental results with real IR images show the robustness of the proposed target detection in real world.

물체 추적을 위한 강화된 부분공간 표현 (Enhanced Representation for Object Tracking)

  • 윤석민;유한주;최진영
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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    • pp.408-410
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    • 2009
  • We present an efficient and robust measurement model for visual tracking. This approach builds on and extends work on subspace representations of measurement model. Subspace-based tracking algorithms have been introduced to visual tracking literature for a decade and show considerable tracking performance due to its robustness in matching. However the measures used in their measurement models are often restricted to few approaches. We propose a novel measure of object matching using Angle In Feature Space, which aims to improve the discriminability of matching in subspace. Therefore, our tracking algorithm can distinguish target from similar background clutters which often cause erroneous drift by conventional Distance From Feature Space measure. Experiments demonstrate the effectiveness of the proposed tracking algorithm under severe cluttered background.

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시차 공간에서 divide-and-conquer 방법을 이용한 스테레오 정합 (Stereo matching using the divide-and-conquer method in the disparity space image)

  • 이종민;김대현;윤용인;최종수
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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    • pp.179-182
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    • 2003
  • This paper proposes a new stereo matching algorithm using both the divide-and-conquer method and the DSI(Disparity Space Image) technique. Firstly, we find salient feature points on the each scanline of the left image and find the corresponding feature point at the right image. Then the problem of a scanline is divided into several subproblems. By this way, matching of the subintervals is implemented by using the DSI technique. The DSI technique for stereo matching process is a very efficient solution to find matches and occlusions simultaneously and it is very speedy. In addition, we apply three occluding patterns to process occluded regions, as a result, we reduce mismatches at the disparity discontinuity.

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The southeastern region of the Vela SNR

  • Kim, Il-Joong;Seon, Kwang-Il;Min, Kyoung-Wook
    • 천문학회보
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    • 제35권2호
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    • pp.69.2-69.2
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    • 2010
  • We investigate the southeastern region of the Vela supernova remnant (SNR) in the multi-wavelength domains. This region is quite interesting because it includes the bullet feature D/D´ and another SNR (the Vela Jr.). The C IV $\lambda\lambda1548$, 1551 emission-line morphologies obtained from the FIMS/SPEAR data show that there are several local peaks of C IV on the bullet D/D´ and the Vela Jr. SNR. This may provide clues to direct interaction between both SNRs. Also, we found that the southeastern side of the Vela is in direct contact with an H-alpha ring feature whose central source seems to be a B-type star, HD 76161. The C IV emission peaks along this contact boundary. We investigate this interacting region in detail.

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잠수함 개념 설계를 위한 디지털 목업 개발 (Development of a Digital Mock-up for Conceptual Design of a Submarine)

  • 김태환;전상후;신동목
    • 한국해양공학회지
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    • 제23권1호
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    • pp.152-157
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    • 2009
  • In designing and manufacturing a submarine, an expensive real mock up is usually built as a reference because of the spatial constraints of a submarine. This paper presents an integrated and automated design process for a submarine that uses a digital mock up. Various equipment libraries are built for feature based design. Using the developed digital mock up, this paper shows various ways to verify the design, including a space analysis to check for any interference between pieces of equipment and the hull and an ergonomic analysis using lifelike dummies to examine the work space and operability. As a part of the integrated design system, a design automation system was also developed to generate surface point data for the outer hull, pressure hull, casing, and sail. The whole process was applied to the design of a submarine for verification.

Feasibility Study of Gait Recognition Using Points in Three-Dimensional Space

  • Kim, Minsung;Kim, Mingon;Park, Sumin;Kwon, Junghoon;Park, Jaeheung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제13권2호
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    • pp.124-132
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    • 2013
  • This study investigated the feasibility of gait recognition using points on the body in three-dimensional (3D) space based on comparisons of four different feature vectors. To obtain the point trajectories on the body in 3D, gait motion data were captured from 10 participants using a 3D motion capture system, and four shoes with different heel heights were used to study the effects of heel height on gait recognition. Finally, the recognition rates were compared using four methods and different heel heights.

One-Class Support Vector Learning and Linear Matrix Inequalities

  • Park, Jooyoung;Kim, Jinsung;Lee, Hansung;Park, Daihee
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제3권1호
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    • pp.100-104
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    • 2003
  • The SVDD(support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to consider the problem of modifying the SVDD into the direction of utilizing ellipsoids instead of balls in order to enable better classification performance. After a brief review about the original SVDD method, this paper establishes a new method utilizing ellipsoids in feature space, and presents a solution in the form of SDP(semi-definite programming) which is an optimization problem based on linear matrix inequalities.