• Title/Summary/Keyword: Hausdorff 거리

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The Recognition of Grapheme 'ㅁ', 'ㅇ' Using Neighbor Angle Histogram and Modified Hausdorff Distance (이웃 각도 히스토그램 및 변형된 하우스도르프 거리를 이용한 'ㅁ', 'ㅇ' 자소 인식)

  • Chang Won-Du;Kim Ha-Young;Cha Eui-Young;Kim Do-Hyeon
    • Journal of Korea Multimedia Society
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    • v.8 no.2
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    • pp.181-191
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    • 2005
  • The classification error of 'ㅁ', 'ㅇ' is one of the main causes of incorrect recognition in Korean characters, but there haven't been enough researches to solve this problem. In this paper, a new feature extraction method from Korean grapheme is proposed to recognize 'ㅁ', 'ㅇ'effectively. First, we defined an optimal neighbor-distance selection measure using modified Hausdorff distance, which we determined the optimal neighbor-distance by. And we extracted neighbor-angle feature which was used as the effective feature to classify the two graphemes 'ㅁ', 'ㅇ'. Experimental results show that the proposed feature extraction method worked efficiently with the small number of features and could recognize the untrained patterns better than the conventional methods. It proves that the proposed method has a generality and stability for pattern recognition.

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Facial Expression Recognition using Hausdorff Distance Matching and Caricatural Effect (하우스도르프 거리매칭과 캐리커쳐 효과를 이용한 얼굴표정 인식)

  • 박주상;오경환
    • Proceedings of the Korean Information Science Society Conference
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    • 2001.04b
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    • pp.526-528
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    • 2001
  • 기존의 얼굴표정 인식연구의 대부분은 얼굴영상에서 사전정보 획득과, 인식이 각각 별개로 수행되어, 전자의 결과가 후자를 보장하지 못하거나, 데이터와 계산 양의 과다, 그리고 인지과정이 사람과 다르다는 등의 문제가 있다. 이에 대해 하우스도르프 거리 매칭을 적용, 표정인식을 시도한다. 이는 전체적인 유사도를 측정하는 방법으로서 전체이론(Holistic theory)에 기반하여, '사람의 인지과정'을 따른다. 그러나 축소된 데이터를 사용하므로, 이 방법의 인식결과가 부족할 경우, 영상워핑을 적용하여 Brennan과 Carton이 제안한 캐리커쳐 효과를 이용한다. 이는 영상을 적절히 변형, 표정의 특징을 과장하고 잡영을 제거하여, 인식하기 쉬운, 분명한 표정을 생성하는 방법이다. 위 과정을 통해, 사람의 인지과정을 모사하고, 최소한의 데이터로써 사전정보 획득과정이 생략된, 입력영상으로부터 직접 표정을 인식하는 방법을 제안한다.

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Efficient Video Retrieval Scheme with Luminance Projection Model (휘도투시모델을 적용한 효율적인 비디오 검색기법)

  • Kim, Sang Hyun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.16 no.12
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    • pp.8649-8653
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    • 2015
  • A number of video indexing and retrieval algorithms have been proposed to manage large video databases efficiently. The video similarity measure is one of most important technical factor for video content management system. In this paper, we propose the luminance characteristics model to measure the video similarity efficiently. Most algorithms for video indexing have been commonly used histograms, edges, or motion features, whereas in this paper, the proposed algorithm is employed an efficient similarity measure using the luminance projection. To index the video sequences effectively and to reduce the computational complexity, we calculate video similarity using the key frames extracted by the cumulative measure, and compare the set of key frames using the modified Hausdorff distance. Experimental results show that the proposed luminance projection model yields the remarkable improved accuracy and performance than the conventional algorithm such as the histogram comparison method, with the low computational complexity.

Improvement OCR Algorithm for Efficient Book Catalog RetrievalTechnology (효과적인 도서목록 검색을 위한 개선된 OCR알고리즘에 관한 연구)

  • HeWen, HeWen;Baek, Young-Hyun;Moon, Sung-Ryong
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.47 no.1
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    • pp.152-159
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    • 2010
  • Existing character recognition algorithm recognize characters in simple conditional. It has the disadvantage that recognition rates often drop drastically when input document image has low quality, rotated text, various font or size text because of external noise or data loss. In this paper, proposes the optical character recognition algorithm which using bicubic interpolation method for the catalog retrieval when the input image has rotated text, blurred, various font and size. In this paper, applied optical character recognition algorithm consist of detection and recognition part. Detection part applied roberts and hausdorff distance algorithm for correct detection the catalog of book. Recognition part applied bicubic interpolation to interpolate data loss due to low quality, various font and size text. By the next time, applied rotation for the bicubic interpolation result image to slant proofreading. Experimental results show that proposal method can effectively improve recognition rate 6% and search-time 1.077s process result.

The Implementation of Face Authentication System Using Real-Time Image Processing (실시간 영상처리를 이용한 얼굴 인증 시스템 구현)

  • Baek, Young-Hyun;Shin, Seong;Moon, Sung-Ryong
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.2
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    • pp.193-199
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    • 2008
  • In this paper, it is proposed the implementation of face authentication system based on real-time image processing. We described the process implementing the two steps for real-time face authentication system. At first face detection steps, we describe the face detection by using feature of wavelet transform, LoG operator and hausdorff distance matching. In the second step we describe the new dual-line principal component analysis(PCA) for real-time face recognition. It is combines horizontal line to vertical line so as to accept local changes of PCA. The proposed system is affected a little by the video size and resolution. And then simulation results confirm the effectiveness of out system and demonstrate its superiority to other conventional algorithm. Finally, the possibility of performance evaluation and real-time processing was confirmed through the implementation of face authentication system.

Geometric Processing for Freeform Surfaces Based on High-Precision Torus Patch Approximation (토러스 패치 기반의 정밀 근사를 이용한 자유곡면의 기하학적 처리)

  • Park, Youngjin;Hong, Q Youn;Kim, Myung-Soo
    • Journal of the Korea Computer Graphics Society
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    • v.25 no.3
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    • pp.93-103
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    • 2019
  • We introduce a geometric processing method for freeform surfaces based on high-precision torus patch approximation, a new spatial data structure for efficient geometric operations on freeform surfaces. A torus patch fits the freeform surface with flexibility: it can handle not only positive and negative curvature but also a zero curvature. It is possible to precisely approximate the surface regardless of the convexity/concavity of the surface. Unlike the traditional method, a torus patch easily bounds the surface normal, and the offset of the torus becomes a torus again, thus helps the acceleration of various geometric operations. We have shown that the torus patch's approximation accuracy of the freeform surface is high by measuring the upper bound of the two-sided Hausdorff distance between the freeform surface and set of torus patches. Using the method, it can be easily processed to detect an intersection curve between two freeform surfaces and find the offset surface of the freeform surface.

Building Identification for 3D Modeling of Urban Area (도심지 3D 모델링을 위한 동일건물 인식)

  • Sohn, Hong-Gyoo;Park, Jung-Hwan;Kim, Ho-Sung
    • 한국공간정보시스템학회:학술대회논문집
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    • 2005.05a
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    • pp.453-457
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    • 2005
  • 3차원 지형공간정보체계에 대한 관심의 증가와 함께 도심지의 3차원 모델링에 관한 다양한 연구가 활발히 진행되고 있다. 단색영상을 용하여 영역기반정합이나 형상기반정합을 실시하던 기존의 3차원 모델링 기법은 오정합이 많이 발생할 수 있으며, 모델링에 소요되는 시간이 많이 걸리는 단점이 있다. 따라서 본 논문에서는 새로운 3D 모델링에 대한 접근법의 하나의 단계로서 컬러영상으로부터 경계정보와 색상정보를 활용하여 동일건물을 인식하는 방법에 대하여 연구를 수행하였다. 경계정보에 대해서는 보완된 Hausdorff 거리 개념을 사용하였으며, 색상정보에 대해서는 수정된 컬러 인덱싱 기법을 사용하였다 IKONOS영상을 사용하여 실험을 실시한 결과 두 가지 정보를 각각 단독으로 사용하는 경우 보다는 두 가지 정보를 조합하여 사용하는 경우 인식이 보다 효과적으로 이루어지는 것을 확인할 수 있었다.

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Object Classification Method using Hilbert Scanning Distance (힐버트 스캔 거리값을 이용한 물체식별 알고리즘)

  • Choi, Jeong-Hwan;Baek, Young-Min;Choi, Jin-Young
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.57 no.4
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    • pp.700-705
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    • 2008
  • In this paper, we propose object classification algorithm for real-time surveillance system. We have approached this problem using silhouette-based template matching. The silhouette of the object is extracted, and then it is compared with representative template models. Template models are previously stored in the database. Our algorithm is similar to previous pixel-based template matching scheme like Hausdorff Distance, but we use 1D image array rather than 2D regions inspired by Hilbert Path. Transformation of images could reduce computational burden to compute similarity between the detected image and the template images. Experimental results show robustness and real-time performance in object classification, even in low resolution images.

Tracking Moving Object using Hierarchical Search Method (계층적 탐색기법을 이용한 이동물체 추적)

  • 방만식;김태식;김영일
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.7 no.3
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    • pp.568-576
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    • 2003
  • This paper proposes a moving object tracking algorithm by using hierarchical search method in dynamic scenes. Proposed algorithm is based on two main steps: generation step of initial model from different pictures, and tracking step of moving object under the time-yawing scenes. With a series of this procedure, tracking process is not only stable under far distance circumstance with respect to the previous frame but also reliable under shape variation from the 3-dimensional(3D) motion and camera sway, and consequently, by correcting position of moving object, tracking time is relatively reduced. Partial Hausdorff distance is also utilized as an estimation function to determine the similarity between model and moving object. In order to testify the performance of proposed method, the extraction and tracking performance have tested using some kinds of moving car in dynamic scenes. Experimental results showed that the proposed algorithm provides higher performance. Namely, matching order is 28.21 times on average, and considering the processing time per frame, it is 53.21ms/frame. Computation result between the tracking position and that of currently real with respect to the root-mean-square(rms) is 1.148. In the occasion of different vehicle in terms of size, color and shape, tracking performance is 98.66%. In such case as background-dependence due to the analogy to road is 95.33%, and total average is 97%.

Performance Evaluation of Automatic Segmentation based on Deep Learning and Atlas according to CT Image Acquisition Conditions (CT 영상획득 조건에 따른 딥 러닝과 아틀라스 기반의 자동분할 성능 평가)

  • Jung Hoon Kim
    • Journal of the Korean Society of Radiology
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    • v.18 no.3
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    • pp.213-222
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    • 2024
  • This study analyzed the volumes generated by deep learning and atlas-based automatic segmentation methods, as well as the Dice similarity coefficient and 95% Hausdorff distance, according to the conditions of conduction voltage and conduction current in computed tomography for lung radiotherapy. The first result, the volumes generated by the atlas-based smart segmentation method showed the smallest volume change as a function of the change in tube voltage and tube current, while Aview RT ACS and OncoStudio using deep learning showed smaller volumes at tube currents lower than 100 mA. The second result, the Dice similarity coefficient, showed that Aview RT ACS was 2% higher than OncoStuido, and the 95% Hausdorff distance results also showed that Aview RT ACS analyzed an average of 0.2-0.5% higher than OncoStudio. However, the standard deviation of the respective results for tube current and tube voltage is lower for OncoStudio, which suggests that the results are consistent across volume variations. Therefore, caution should be exercised when using deep learning-based automatic segmentation programs at low perfusion voltages and low perfusion currents in CT imaging conditions for lung radiotherapy, and similar results were obtained with conventional atlas-based automatic segmentation programs at certain perfusion voltages and perfusion currents.