• Title/Summary/Keyword: Recognition Distance

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Face Recognition Based on Weighted Hausdorff Distance for Profile Image (가중치 하우스도르프 거리를 이용한 프로파일 얼굴인식)

  • 이영학
    • Journal of Korea Multimedia Society
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    • v.7 no.4
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    • pp.474-483
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    • 2004
  • In this paper, we present a new Practical implementation of a person verification system using the profile of 3-dimensional(3D) face images based on weighted Hausdorff distance(WHD) used depth information. The approach works on finding the nose tip have protrusion shape on the face using iterative selection method to use a fiducial feint and extract the profile image from vertical 3D data for the nose tip. Hausdorff distance(HD) is one of usually used measures for object matching. This works analyze the conventional HD and WHD, which the weighted factor is depth information. The Ll measure for comparing two feature vectors were used, because it is simple and robust. In the experimental results, the WHD method achieves recognition rate of 94.3% when the ranked threshold is 5.

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An Improved Object Detection Method using Hausdorff Distance Modified by Local Pattern Similarity (국지적 패턴 유사도에 의해 수정된 Hausdorff 거리를 이용한 개선된 객체검출)

  • Cho, Kyoung-Sik;Koo, Ja-Young
    • Journal of the Korea Society of Computer and Information
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    • v.12 no.6
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    • pp.147-152
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    • 2007
  • Face detection is a crucial part of the face recognition system. It determines the performance of the whole recognition system. Hausdorff distance metric has been used in face detection and recognition with good results. It defines the distance metric based only on the geometric similarity between two sets or points. However, not only the geometry but also the local patterns around the points are available in most cases. In this paper a new Hausdorff distance measure is proposed that makes hybrid use of the similarity of the geometry and the local patterns around the points. Several experiments shows that the new method outperforms the conventional method.

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Face recognition rate comparison with distance change using embedded data in stereo images (스테레오 영상에서 임베디드 데이터를 이용한 거리에 따른 얼굴인식률 비교)

  • 박장한;남궁재찬
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.41 no.6
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    • pp.81-89
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    • 2004
  • In this paper, we compare face recognition rate by PCA algorithm using distance change and embedded data being input left side and right side image in stereo images. The proposed method detects face region from RGB color space to YCbCr color space. Also, The extracted face image's scale up/down according to distance change and extracts more robust face region. The proposed method through an experiment could establish standard distance (100cm) in distance about 30∼200cm, and get 99.05% (100cm) as an average recognition result by scale change. The definition of super state is specification region in normalized size (92${\times}$112), and the embedded data extracts the inner factor of defined super state, achieved face recognition through PCA algorithm. The orignal images can receive specification data in limited image's size (92${\times}$112) because embedded data to do learning not that do all learning, in image of 92${\times}$112 size averagely 99.05%, shows face recognition rate of test 1 99.05%, test 2 98.93%, test 3 98.54%, test 4 97.85%. Therefore, the proposed method through an experiment showed that if apply distance change rate could get high recognition rate, and the processing speed improved as well as reduce face information.

Feature Generation Method for Low-Resolution Face Recognition (저해상도 얼굴 영상의 인식을 위한 특징 생성 방법)

  • Choi, Sang-Il
    • Journal of Korea Multimedia Society
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    • v.18 no.9
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    • pp.1039-1046
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    • 2015
  • We propose a feature generation method for low-resolution face recognition. For this, we first generate new features from the input features (pixels) of a low-resolution face image by adding the higher-order terms. Then, we evaluate the separability of both of the original input features and new features by computing the discriminant distance of each feature. Finally, new data sample used for recognition consists of the features with high separability. The experimental results for the FERET, CMU-PIE and Yale B databases show that the proposed method gives good recognition performance for low-resolution face images compared with other method.

The Validation of Speech Recognition Performance Change according to the kind and established distance of the Microphone (마이크로폰의 종류 및 설치거리에 따른 음성인식성능변화의 검토)

  • Kim Yoen-Whoa;Lee Kwang-Hyun;Choi Dae-Lim;Kim Bong-Wan;Lee Yong-Ju
    • Proceedings of the KSPS conference
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    • 2003.10a
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    • pp.141-143
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    • 2003
  • Speech recognition performance depends on various factors. One of the factors is the characteristic and established distance of a microphone which is used when speech data is collected. Thus, in the present experiment speech databases for tests are created through the type and established distance of a microphone. Then, acoustic models are built based on these databases, and each of the acoustic models is assessed by the data to determine recognition performance depending on various microphones and established microphone distances.

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Comparing object images using fuzzy-logic induced Hausdorff Distance (퍼지 논리기반 HAUSDORFF 거리를 이용한 물체 인식)

  • 강환일
    • Journal of Intelligence and Information Systems
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    • v.6 no.1
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    • pp.65-72
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    • 2000
  • In this paper we propose the new binary image matching algorithm called the Fuzzy logic induced Hausdorff Distance(FHD) for finding the maximally matched image with the query image. The membership histogram is obtained by normalizing the cardinality of the subset with the corresponding radius after obtaining the distribution of the minimum distance computed by the Hausdroff distance between two binary images. in the proposed algorithm, The fuzzy influence method Center of Gravity(COG) is applied to calculate the best matching candidate in the membership function described above. The proposed algorithm shows the excellent results for the face image recognition when the noise is added to the query image as well as for the character recognition.

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An Recognition and Acquisition method of Distance Information in Direction Signs for Vehicle Location (차량의 위치 파악을 위한 도로안내표지판 인식과 거리정보 습득 방법)

  • Kim, Hyun-Tae;Jeong, Jin-Seong;Jang, Young-Min;Cho, Sang-Bock
    • Journal of the Institute of Electronics and Information Engineers
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    • v.54 no.1
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    • pp.70-79
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    • 2017
  • This study proposes a method to quickly and accurately acquire distance information on direction signs. The proposed method is composed of the recognition of the sign, pre-processing to facilitate the acquisition of the road sign distance, and the acquisition of the distance data. The road sign recognition uses color detection including gamma correction in order to mitigate various noise issues. In order to facilitate the acquisition of distance data, this study applied tilt correction using linear factors, and resolution correction using Fourier transform. To acquire the distance data, morphological operation was used to highlight the area, along with labeling and template matching. By acquiring the distance information on the direction sign through such a processes, the proposed system can be output the distance remaining to the next junction. As a result, when the proposed method is applied to system it can process the data in real-time using the fast calculation speed, average speed was shown to be 0.46 second per frame, with accuracy of 0.65 in similarity value.

Development of Position Awareness Algorithm Using Improved Trilateration Measurement Method (개선된 삼변측량법을 이용한 위치인지 알고리즘 개발)

  • Sohn, Jong-Hoon;Hwang, Gi-Hyun
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.17 no.2
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    • pp.473-480
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    • 2013
  • In this paper, location recognition algorithm is developed to improve the accuracy using improve Trilateration. The location recognition algorithm is first calculate the location refer to the measured signal power. Error can be occurred when measure distance with arranged node in specific location. If the distance data is received from node (receiver, coordinator), Node selected for location calculation is defined through section. If the distance data is received from node (receiver, coordinator), Node selected for location calculation is defined through section. Second, we apply algorithm of section filtering. If there are 4 sections in node, we consider 1 section to 6 location recognition coordinates. A special characteristic drawback of RF is that the actual distance is actually farther than the calculated received distance data. This is error is incurred when the signal strength increases. We reduce the location recognition error by applying an improved algorithm as secondary after filtering primary through section filtering.

A Study on the Pattern Recognition based Distance Protective Relaying Scheme in Power System (전력계통의 패턴인식형 거리계전기법에 관한 연구)

  • 이복구;윤석무;박철원;신명철
    • Journal of the Korean Institute of Intelligent Systems
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    • v.8 no.2
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    • pp.9-20
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    • 1998
  • In this paper, a new distance relaying scheme is proposed. Artificial neural networks are applied to the distance relaying system composed of pattern recognition based. The proposed distance relaying scheme has two blocks of pattern recognition stages to estimate the fundamental frequency and to classify the fault types. In the first block, a filtering method using neural networks called a neural networks mapping filter(NMF) is presented to efficiently extract the features. And in the sec'ond block, the estimator called neural networks fault pattern estimator(NFPE) is also presented to classify the fault types by the extracted effective features obtained from NMF. Each block of these applied schemes is trained by back-propagation algorithm of multilayer perceptron and show the fast and accurate pattern recognition by ability of multilayer neural networks. The test result of this approach are obtained the good performance from the fault transient wave signals of EMTP(e1ectromagnetic transients program) in the various fault conditions of power systems.

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Finger Detection using a Distance Graph (거리 그래프를 이용한 손가락 검출)

  • Song, Ji-woo;Oh, Jeong-su
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.20 no.10
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    • pp.1967-1972
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    • 2016
  • This paper defines a distance graph for a hand region in a depth image and proposes an algorithm detecting finger using it. The distance graph is a graph expressing the hand contour with angles and Euclidean distances between the center of palm and the hand contour. Since the distance graph has local maximum at fingertips' position, we can detect finger points and recognize the number of them. The hand contours are always divided into 360 angles and the angles are aligned with the center of the wrist as a starting point. And then the proposed algorithm can well detect fingers without influence of the size and orientation of the hand. Under some limited recognition test conditions, the recognition test's results show that the recognition rate is 100% under 1~3 fingers and 98% under 4~5 fingers and that the failure case can also be recognized by simple conditions to be available to add.