• Title, Summary, Keyword: Feature Extraction

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Optimal Feature Extraction for Multiclass Problems through Proper Choice of Initial Feature Vectors (초기 피춰벡터 설정을 통한 다중클래스 문제에 대한 최적 피춰 추출 기법)

  • 최의선;이철희
    • Proceedings of the IEEK Conference
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    • pp.647-650
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    • 1999
  • In this Paper, we propose an optimal feature extraction for multiclass problems through proper choice of initial feature vectors. Although numerous feature extraction algorithms have been proposed, those algorithms are not optimal for multiclass problems. Recently, an optimal feature extraction algorithm for multiclass problems has been proposed, which provides a better performance than the conventional feature extraction algorithms. In this paper, we improve the algorithm by choosing good initial feature vectors. As a result, the searching time is significantly reduced. The chance to be stuck in a local minimum is also reduced.

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Feature-based Extraction of Machining Features (특징형상 접근방법에 의한 가공특징형상 추출)

  • 이재열;김광수
    • Korean Journal of Computational Design and Engineering
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    • v.4 no.2
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    • pp.139-152
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    • 1999
  • This paper presents a feature-based approach to extracting machining features fro a feature-based design model. In the approach, a design feature to machining feature conversion process incrementally converts each added design feature into a machining feature or a set of machining features. The proposed approach an efficiently handle protrusion features and interacting features since it takes advantage of design feature information, design intent, and functional requirements during feature extraction. Protrusion features cannot be directly mapped into machining features so that the removal volumes surrounding protrusion features are extracted and converted it no machining features. By utilizing feature information as well as geometry information during feature extraction, the proposed approach can easily overcome inherent problems relating to feature recognition such as feature interactions and loss of design intent. In addition, a feature extraction process can be simplified, and a large set of complex part can be handled with ease.

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Facial Feature Extraction with Its Applications

  • Lee, Minkyu;Lee, Sangyoun
    • Journal of International Society for Simulation Surgery
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    • v.2 no.1
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    • pp.7-9
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    • 2015
  • Purpose In the many face-related application such as head pose estimation, 3D face modeling, facial appearance manipulation, the robust and fast facial feature extraction is necessary. We present the facial feature extraction method based on shape regression and feature selection for real-time facial feature extraction. Materials and Methods The facial features are initialized by statistical shape model and then the shape of facial features are deformed iteratively according to the texture pattern which is selected on the feature pool. Results We obtain fast and robust facial feature extraction result with error less than 4% and processing time less than 12 ms. The alignment error is measured by average of ratio of pixel difference to inter-ocular distance. Conclusion The accuracy and processing time of the method is enough to apply facial feature based application and can be used on the face beautification or 3D face modeling.

Robust Feature Extraction and Tracking Algorithm Using 2-dimensional Wavelet Transform (2차원 웨이브릿 변환을 이용한 강건한 특징점 추출 및 추적 알고리즘)

  • Jang, Sung-Kun;Suk, Jung-Youp
    • Proceedings of the IEEK Conference
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    • pp.405-406
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    • 2007
  • In this paper, we propose feature extraction and tracking algorithm using multi resolution in 2-dimensional wavelet domain. Feature extraction selects feature points using 2-level wavelet transform in interested region. Feature tracking estimates displacement between current frame and next frame based on feature point which is selected feature extraction algorithm. Experimental results show that the proposed algorithm confirmed a better performance than the existing other algorithms.

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Feature Extraction for Iris Recognition by Using Statistical Methods (통계적 홍채 특징 추출 방법)

  • 배광혁;이철한;노승인;김재희
    • Proceedings of the IEEK Conference
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    • pp.61-64
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    • 2002
  • In this paper, we propose the iris feature extraction by using statistical methods. There are many approaches for iris feature extraction, but most of them require a set of parameters that one should choose for the transformation to obtain a useful representation of the iris. It would be most useful to estimate the method of the iris feature extraction from iris itself. Therefore, we apply the unsupervised statistical methods for the iris feature extraction.

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Image Feature Extraction Using Energy field Analysis (에너지장 해석을 통한 영상 특징량 추출 방법 개발)

  • 김면희;이태영;이상룡
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.404-406
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    • 2002
  • In this paper, the method of image feature extraction is proposed. This method employ the energy field analysis, outlier removal algorithm and ring projection. Using this algorithm, we achieve rotation-translation-scale invariant feature extraction. The force field are exploited to automatically locate the extrema of a small number of potential energy wells and associated potential channels. The image feature is acquired from relationship of local extrema using the ring projection method.

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Feature Extraction of ECG Signal for Heart Diseases Diagnoses (심장질환진단을 위한 ECG파형의 특징추출)

  • Kim, Hyun-Dong;Min, Chul-Hong;Kim, Tae-Seon
    • Proceedings of the KIEE Conference
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    • pp.325-327
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    • 2004
  • ECG limb lead II signal widely used to diagnosis heart diseases and it is essential to detect ECG events (onsets, offsets and peaks of the QRS complex P wave and T wave) and extract them from ECG signal for heart diseases diagnoses. However, it is very difficult to develop standardized feature extraction formulas since ECG signals are varying on patients and disease types. In this paper, simple feature extraction method from normal and abnormal types of ECG signals is proposed. As a signal features, heart rate, PR interval, QRS interval, QT interval, interval between S wave and baseline, and T wave types are extracted. To show the validity of proposed method, Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Sinus Bradycardia, and Sinus Tachycardia data from MIT-BIH arrhythmia database are used for feature extraction and the extraction results showed higher extraction capability compare to conventional formula based extraction method.

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The Application of SVD for Feature Extraction (특징추출을 위한 특이값 분할법의 응용)

  • Lee Hyun-Seung
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.43 no.2
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    • pp.82-86
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    • 2006
  • The design of a pattern recognition system generally involves the three aspects: preprocessing, feature extraction, and decision making. Among them, a feature extraction method determines an appropriate subspace of dimensionality in the original feature space of dimensionality so that it can reduce the complexity of the system and help to improve successful recognition rates. Linear transforms, such as principal component analysis, factor analysis, and linear discriminant analysis have been widely used in pattern recognition for feature extraction. This paper shows that singular value decomposition (SVD) can be applied usefully in feature extraction stage of pattern recognition. As an application, a remote sensing problem is applied to verify the usefulness of SVD. The experimental result indicates that the feature extraction using SVD can improve the recognition rate about 25% compared with that of PCA.

Recent Advances in Feature Detectors and Descriptors: A Survey

  • Lee, Haeseong;Jeon, Semi;Yoon, Inhye;Paik, Joonki
    • IEIE Transactions on Smart Processing and Computing
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    • v.5 no.3
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    • pp.153-163
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    • 2016
  • Local feature extraction methods for images and videos are widely applied in the fields of image understanding and computer vision. However, robust features are detected differently when using the latest feature detectors and descriptors because of diverse image environments. This paper analyzes various feature extraction methods by summarizing algorithms, specifying properties, and comparing performance. We analyze eight feature extraction methods. The performance of feature extraction in various image environments is compared and evaluated. As a result, the feature detectors and descriptors can be used adaptively for image sequences captured under various image environments. Also, the evaluation of feature detectors and descriptors can be applied to driving assistance systems, closed circuit televisions (CCTVs), robot vision, etc.

Study of Nonlinear Feature Extraction for Faults Diagnosis of Rotating Machinery (회전기계의 결함진단을 위한 비선형 특징 추출 방법의 연구)

  • Widodo, Achmad;Yang, Bo-Suk
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • pp.127-130
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    • 2005
  • There are many methods in feature extraction have been developed. Recently, principal components analysis (PCA) and independent components analysis (ICA) is introduced for doing feature extraction. PCA and ICA linearly transform the original input into new uncorrelated and independent features space respectively In this paper, the feasibility of using nonlinear feature extraction will be studied. This method will employ the PCA and ICA procedure and adopt the kernel trick to nonlinearly map the data into a feature space. The goal of this study is to seek effectively useful feature for faults classification.

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