• Title/Summary/Keyword: 특징 벡터

Search Result 911, Processing Time 0.075 seconds

Mounted PCB Pattern Recognition System Using Neural Network (신경망을 이용한 실장 PCB 패턴인식 시스템)

  • 김상철;정성환
    • Proceedings of the Korea Multimedia Society Conference
    • /
    • 1998.04a
    • /
    • pp.411-416
    • /
    • 1998
  • 본 논문은 Wavelet 변환 영역에서 특징 벡터를 추출하여 ART2 신경회로망으로 실장 PCB 패턴을 인식하는 알고리즘을 제안한다. PCB 형태 정보는 Wavelet에 의해 주파수 영역으로 변환되고, 이들 계수 행렬로부터 특징 벡터로서 추출된다. ART2 신경회로망은 이러한 특징 벡터들을 입력벡터로 사용하여 인식한다. 실장 PCB 영상 55장을 사용하여 실험한 결고, 학습된 입력패턴은 물론 비학습 입력패턴에 대해서도 약 99%의 인식율을 얻었다. 또한 제안된 방법은 Wavelet 변환 영역사에서 수직, 수평, 대각선 정보만으로 특징 벡터를 구축함으로써 특징 추출 과정이 비교적 간단하고 특징 벡터의 수도 줄일 수 있어, 효과적인 특징벡터의 추출이 가능함을 보였다.

  • PDF

Contents-based Image Retrieval Using Regression of Shape Features (모양 정보의 회귀추정에 의한 내용 기반 이미지 검색 기법)

  • Song Jun-Kyu;Choi Hwang-Kyu
    • Journal of Digital Contents Society
    • /
    • v.2 no.2
    • /
    • pp.157-166
    • /
    • 2001
  • In this paper we propose a feature vector extraction technique using regression of shape features for the content-based image retrieval system. The proposed technique can reduce the number of dimensions of a feature vector by converting the extracted high-dimensional feature vector into a specific n-dimensional feature vector. This paper shows how to resolve the 'dimensionality curse' problem by reducing the number of dimensions of a feature vector, and shows that the technique is more efficient than the conventional techniques for the practical image retrievals.

  • PDF

Robust Face Recognition based on Gabor Feature Vector illumination PCA Model (가버 특징 벡터 조명 PCA 모델 기반 강인한 얼굴 인식)

  • Seol, Tae-In;Kim, Sang-Hoon;Chung, Sun-Tae;Jo, Seong-Won
    • Journal of the Institute of Electronics Engineers of Korea SC
    • /
    • v.45 no.6
    • /
    • pp.67-76
    • /
    • 2008
  • Reliable face recognition under various illumination environments is essential for successful commercialization. Feature-based face recognition relies on a good choice of feature vectors. Gabor feature vectors are known to be more robust to variations of pose and illumination than any other feature vectors so that they are popularly adopted for face recognition. However, they are not completely independent of illuminations. In this paper, we propose an illumination-robust face recognition method based on the Gabor feature vector illumination PCA model. We first construct the Gabor feature vector illumination PCA model where Gator feature vector space is rendered to be decomposed into two orthogonal illumination subspace and face identity subspace. Since the Gabor feature vectors obtained by projection into the face identity subspace are separated from illumination, the face recognition utilizing them becomes more robust to illumination. Through experiments, it is shown that the proposed face recognition based on Gabor feature vector illumination PCA model performs more reliably under various illumination and Pose environments.

Mobile Object Tracking Algorithm Using Particle Filter (Particle filter를 이용한 이동 물체 추적 알고리즘)

  • Kim, Se-Jin;Joo, Young-Hoon
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.19 no.4
    • /
    • pp.586-591
    • /
    • 2009
  • In this paper, we propose the mobile object tracking algorithm based on the feature vector using particle filter. To do this, first, we detect the movement area of mobile object by using RGB color model and extract the feature vectors of the input image by using the KLT-algorithm. And then, we get the first feature vectors by matching extracted feature vectors to the detected movement area. Second, we detect new movement area of the mobile objects by using RGB and HSI color model, and get the new feature vectors by applying the new feature vectors to the snake algorithm. And then, we find the second feature vectors by applying the second feature vectors to new movement area. So, we design the mobile object tracking algorithm by applying the second feature vectors to particle filter. Finally, we validate the applicability of the proposed method through the experience in a complex environment.

Moving Object Tracking Method Using Feature Vector (특징 벡터를 이용한 이동 물체 추적)

  • Kim, Se-Jin;Jeon, Hyung-Suk;Joo, Young-Hoon;Park, Jin-Bae
    • Proceedings of the KIEE Conference
    • /
    • 2009.07a
    • /
    • pp.1845_1846
    • /
    • 2009
  • 본 논문에서는 특징 벡터를 이용한 강인한 물체 추적 방법을 제안한다. 먼저, 초기 이동 물체의 움직임 영역을 추출하고, KLT알고리즘을 입력 영상에 적용시켜 특징 벡터들을 추출한다. 초기 추출된 이동 물체의 움직임 영역에 추출된 특징 벡터를 적용시켜 1차 정규화 한다. 그 후, RGB 칼라모델과 HSI 칼라모델을 이용하여 이동 물체에 대한 Blob 영역을 설정하고 설정된 Blob 영역에 대해 1차 특징벡터를 Snake 알고리즘으로 동정하여 2차 정규화 과정을 마무리 한다. 최종 정규화 된 특징 벡터를 Particle filter에 입력 데이터로 이용하여 이동 물체를 추적 한다. 마지막으로, 복잡한 환경에서 실험을 통해 그 응용 가능성을 증명한다.

  • PDF

Feature Selection for Multi-Class Genre Classification using Gaussian Mixture Model (Gaussian Mixture Model을 이용한 다중 범주 분류를 위한 특징벡터 선택 알고리즘)

  • Moon, Sun-Kuk;Choi, Tack-Sung;Park, Young-Cheol;Youn, Dae-Hee
    • The Journal of Korean Institute of Communications and Information Sciences
    • /
    • v.32 no.10C
    • /
    • pp.965-974
    • /
    • 2007
  • In this paper, we proposed the feature selection algorithm for multi-class genre classification. In our proposed algorithm, we developed GMM separation score based on Gaussian mixture model for measuring separability between two genres. Additionally, we improved feature subset selection algorithm based on sequential forward selection for multi-class genre classification. Instead of setting criterion as entire genre separability measures, we set criterion as worst genre separability measure for each sequential selection step. In order to assess the performance proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigate classification performance by GMM classifier and k-NN classifier for selected features using conventional algorithm and proposed algorithm. Proposed algorithm showed improved performance in classification accuracy up to 10 percent for classification experiments of low dimension feature vector especially.

Study on ERP Detection Algorithm Using SVM with wavelet feature vector (웨이블릿 특징 벡터 기반 SVM을 이용한 ERP 검출 알고리즘에 관한 연구)

  • Lee, Young-Seok
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
    • /
    • v.10 no.1
    • /
    • pp.9-15
    • /
    • 2017
  • In this study we performed the experiment to detect the ERP using SVM with wavelet features. The EEG signal that is generated visual stimulated ERP database in SCCN applied for the experiment. The feature vectors for experiment are categorized frequency and continuous wavelet- based vectors. In experimental results, the detection rate of SVM with wavelet feature vectors improved above 10% comparing with frequency- based feature vector. Based on the experimental results we analyzed the relation between the activity degree of the ERP and the band split characteristics of the ERP by wavelet transform.

A Comparison Study on Back-Propagation Neural Network and Support Vector Machines for the Image Classification Problems (영상분류문제를 위한 역전파 신경망과 Support Vector Machines의 비교 연구)

  • Seo, Kwang-Kyu
    • Journal of the Korea Academia-Industrial cooperation Society
    • /
    • v.9 no.6
    • /
    • pp.1889-1893
    • /
    • 2008
  • This paper explores the classification performance of applying to support vector machines (SVMs) for the image classification problems. In this study, we extract the color, texture and shape features of natural images and compare the performance of image classification using each individual feature and integrated features. The experiment results show that classification accuracy on the basis of color feature is better than that based on texture and shape features and the results of the integrating features also provides a better and more robust performance than individual feature. In additions, we show that the proposed classifier of SVM based approach outperforms BPNN to corporate the image classification problems.

Efficient Multi-Object Trajectory Using Robust Color Relationship Feature Vector (칼라 관계 특징벡터를 사용한 효율적인 멀티오브젝트 추적)

  • 김민철;최창규;류상률;김승호
    • Proceedings of the Korean Information Science Society Conference
    • /
    • 2004.10b
    • /
    • pp.778-780
    • /
    • 2004
  • 본 논문에서는 오브젝트가 서로 겹쳤다가 분리되는 상황 하에서도 오브젝트를 정확히 추적할 수 있는 칼라관계(color relationship)특징 벡터를 제안한다. 오브젝트의 정확한 추적경로와 이벤트 검출을 위하여 신뢰성 있는 특징 벡터 추출은 필수적이다. 향상된 오브젝트 추적을 위해 면적. 크기뿐만 아니라 본 논문에서 제안한 칼라관계 특징 벡터를 사용한다. 실험 영상에 적용한 결과 제안된 방법을 사용하였을 경우 멀티오브젝트의 영상에서 겹침(occlusion)과 나타남(disocclusion)이 발생하는 경우에도 정확한 경로 추적이 이루어짐을 볼 수 있었다

  • PDF

남녀의 음향학적 특징벡터의 비교 분석에 관한 연구

  • Choe, Jae-Seung;Jeong, Byeong-Gu
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • 2012.05a
    • /
    • pp.887-890
    • /
    • 2012
  • 본 논문에서는 켑스트럼 계수의 변화에 따른 남성화자와 여성화자의 음향학적인 특징벡터를 비교하여 분석하는 기초적인 연구를 수행한다. 특히 FFT 켑스트럼 및 LPC 켑스트럼에 대한 남녀의 음향학적인 특징벡터의 차이점을 나타낸다. 향후 이러한 차이점을 기초로 하여 신경회로망 등에 의한 성별 인식에 대한 연구를 수행함으로써 남성화자 및 여성화자를 분리할 수 있는 근거를 마련하는 기초연구이다.

  • PDF