• 제목/요약/키워드: direction feature

검색결과 593건 처리시간 0.029초

적응적 가중치에 의한 특징점 추적 알고리즘 (A Feature Tracking Algorithm Using Adaptive Weight Adjustment)

  • 정종면;문영식
    • 전자공학회논문지S
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    • 제36S권11호
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    • pp.68-78
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    • 1999
  • 본 논문에서는 동영상에서 특징점의 궤적을 추적하기 위한 알고리즘을 제안한다. 기존의 방법에서 사용된 대부분의 정합의 척도(matching measure)는 동영상의 움직임 특성을 정확히 반영하지 못하여 잘못된 궤적을 나타내는 경우가 있다. 본 논문에서는 특징범의 공간좌표, 이동방향과 이동거리 등 3가지 속성을 정합에 사용하는데 이들 속성에 대하여 가중치(weight)가 부여된 Euclidean 거리를 정합의 척도로 사용한다. 이때 3가지 속성에 대한 가중치를 움직임의 특성에 따라 적응적으로 변화시켜 줌으로써 강건하게 특징점을 추적할 수 있도록 한다. 제안하는 알고리즘은 매 프레임마다 특징점의 운동특성을 정확히 반영함으로써 기존의 방법에 비해 정확한 궤적을 찾을 수 있으며 이는 다양한 동영상에 대한 실험을 통해 확인되었다.

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한글 특징점 추출을 위한 일반화된 표본화 알고리즘을 이용한 수정된 Hough Transform에 관한 연구 (A study on the modified hough transform for hangul feature extraction using generalized sampling rule)

  • 구하성;고형화
    • 전자공학회논문지B
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    • 제31B권9호
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    • pp.142-149
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    • 1994
  • Hangul is expressed by the basic elements, twenty-four characters. Because these characters are composed of a circle and lines, Hough transform(HT), which has a powerful performance on the noise in extracting lines, is introduced. Many difficulties often occur when the original HT is used to extract strokes and it's direction, position and length from handwritten Hangul characters. Original HT has eight direction selected as samples in the transformed image should be calculated for these eight directions. In this paper, the generalized sampling rule is suggested. According to the rule, those directions which are possible to a line are the only thing to be calculated. The experoment result turned out to be higher than the method that Chen suggested in sampling rate. Anogher experiment result is done on the 1800 handwritten Hangul characters that 10 persons wrote. By feature extracting the oritinal HT and sampling HT. And as a result of six type classification, the suggested method came out higher than original HT.

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방향 연결성 추적을 이용한 의사 특징점 제거 (pseudo feature point removal using direction connectivity tracing)

  • 김강;이건익
    • 한국컴퓨터정보학회:학술대회논문집
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    • 한국컴퓨터정보학회 2011년도 제43차 동계학술발표논문집 19권1호
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    • pp.69-72
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    • 2011
  • 본 논문에서는 방향 연결성 추적을 이용한 의사 특징점 제거에 관하여 연구하였다. 특징점을 추출하는 방법에는 교차수를 이용한 방법이 있다. 그러나 교차수를 이용한 방법에서는 의사 특징점이 많이 추출된다. 교차수를 이용한 방법에서 잘못 추출된 특징점들을 방향 연결성 추적을 이용한 의사 특징점 제거 알고리즘을 이용하여 의사 특징점을 제거하였다. 성능 평가를 위하여 교차수를 이용한 방법과 방향 연결성 추적을 이용하여 추출된 실제 특징점을 비교하였으며, 실험결과 방향 연결성 추적을 이용하여 많은 의사 특징점이 제거되었음을 알 수 있었다.

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가공시간에 의한 복합특징형상의 가공순서 생성 (Machining Sequence Generation with Machining Times for Composite Features)

  • 서영훈;최후곤
    • 한국CDE학회논문집
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    • 제6권4호
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    • pp.244-253
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    • 2001
  • For more complete process planning, machining sequence determination is critical to attain machining economics. Although many studies have been conducted in recent years, most of them suggests the non-unique machining sequences. When the tool approach directions(TAD) are considered fur a feature, both machining time and number of setups can be reduced. Then, the unique machining sequence can be extracted from alternate(non-unique) sequences by minimizing the idle time between operations within a sequence. This study develops an algorithm to generate the best machining sequence for composite prismatic features in a vertical milling operation. The algorithm contains five steps to produce an unique sequence: a precedence relation matrix(PRM) development, tool approach direction determination, machining time calculation, alternate machining sequence generation, and finally, best machining sequence generation with idle times. As a result, the study shows that the algorithm is effective for a given composite feature and can be applicable fur other prismatic parts.

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크기 비교를 통한 차량 식별 (Car Identification Using Comparing Car Size)

  • 신광성;신성윤
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2019년도 춘계학술대회
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    • pp.488-489
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    • 2019
  • 차량의 특징점들 사이의 간격과 그 크기의 비례율의 식으로 자동차의 차종을 식별하는 방법을 제안한다. 자동차 영상은 기본 RGB모델에서 Gray색상 모델로 변환시켜 사용한다. Canny Edge Direction을 수행하여 자동차의 배경이 되는 영상을 제거한다. 윤곽선 추출을 통하여 원하는 특징 점을 얻는다.

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Selection of features and hidden Markov model parameters for English word recognition from Leap Motion air-writing trajectories

  • Deval Verma;Himanshu Agarwal;Amrish Kumar Aggarwal
    • ETRI Journal
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    • 제46권2호
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    • pp.250-262
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    • 2024
  • Air-writing recognition is relevant in areas such as natural human-computer interaction, augmented reality, and virtual reality. A trajectory is the most natural way to represent air writing. We analyze the recognition accuracy of words written in air considering five features, namely, writing direction, curvature, trajectory, orthocenter, and ellipsoid, as well as different parameters of a hidden Markov model classifier. Experiments were performed on two representative datasets, whose sample trajectories were collected using a Leap Motion Controller from a fingertip performing air writing. Dataset D1 contains 840 English words from 21 classes, and dataset D2 contains 1600 English words from 40 classes. A genetic algorithm was combined with a hidden Markov model classifier to obtain the best subset of features. Combination ftrajectory, orthocenter, writing direction, curvatureg provided the best feature set, achieving recognition accuracies on datasets D1 and D2 of 98.81% and 83.58%, respectively.

영상데이타를 이용한 항공기 자세각 추정 (Attitude Estimation of an Aircraft using Image Data)

  • 박성수
    • 한국항공운항학회지
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    • 제19권4호
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    • pp.44-50
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    • 2011
  • This paper presents the algorithm for attitude determination of an aircraft using binary image. An image feature vector, which is invariant to translation, scale and rotation, is constructed to capture the functional relations between the feature vector and the corresponding aircraft attitude. An iterated least squares method is suggested for estimating the attitude of given aircraft using the constructed feature vector library. Simulation results show that the proposed algorithm yields good estimates of aircraft attitude in most viewing range, although a relatively large error occurs in some limited viewing direction.

신호처리를 이용한 웨이퍼 다이싱 상태 모니터링 (Wafer Dicing State Monitoring by Signal Processing)

  • 고경용;차영엽;최범식
    • 한국정밀공학회지
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    • 제17권5호
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    • pp.70-75
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    • 2000
  • After the patterning and probe process of wafer have been achieved, the dicing process is necessary to separate chips from a wafer. The dicing process cuts a wafer to lengthwise and crosswise direction to make many chips by using narrow circular rotating diamond blade. But inferior goods are made under the influence of complex dicing environment such as blade, wafer, cutting water and cutting conditions. This paper describes a monitoring algorithm using feature extraction in order to find out an instant of vibration signal change when bad dicing appears. The algorithm is composed of two steps: feature extraction and decision. In the feature extraction, two features processed from vibration signal which is acquired by accelerometer attached on blade head are proposed. In the decision. a threshold method is adopted to classify the dicing process into normal and abnormal dicing. Experiment have been performed for GaAs semiconductor wafer. Based upon observation of the experimental results, the proposed scheme shown a good accuracy of classification performance by which the inferior goods decreased from 35.2% to 12.8%.

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염색체 영상의 재구성과 특징 파라메타 추출 (Chromosome images Reconstitution and Feature Parameter Extraction)

  • 장용훈;이권순;이영진;전계록;엄상희
    • 대한의용생체공학회:학술대회논문집
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    • 대한의용생체공학회 1996년도 춘계학술대회
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    • pp.103-107
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    • 1996
  • In this paper, We propose an algorithm for reconstitution of chromosome images to extract its morphological feature parameters. It is reconstituted from 460 chromosome images using the 32 direction line algorithm. We extract three morphological feature parameters such as centromeric index, relative length ratio, and relative area ratio. The experiment results show that our method is batter than that of other researchers comparing with the error of feature parameters.

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Face Recognition Using Feature Information and Neural Network

  • Chung, Jae-Mo;Bae, Hyeon;Kim, Sung-Shin
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2001년도 ICCAS
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    • pp.55.2-55
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    • 2001
  • The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region efface candidate. The feature information in the region of face candidate is used to detect a face region. In the recognition step, as a tested, the 360 images of 30 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression, Input variables of the neural networks are the feature information that comes from the eigenface spaces. The simulation results of 30 persons show that the proposed method yields high recognition rates.

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