• Title/Summary/Keyword: Feature extraction

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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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    • 2002.06d
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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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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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    • 1999.06a
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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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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.

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 s.308
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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.

Laver Farm Feature Extraction From Landsat ETM+ Using Independent Component Analysis

  • Han J. G.;Yeon Y. K.;Chi K. H.;Hwang J. H.
    • Proceedings of the KSRS Conference
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    • 2004.10a
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    • pp.359-362
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    • 2004
  • In multi-dimensional image, ICA-based feature extraction algorithm, which is proposed in this paper, is for the purpose of detecting target feature about pixel assumed as a linear mixed spectrum sphere, which is consisted of each different type of material object (target feature and background feature) in spectrum sphere of reflectance of each pixel. Landsat ETM+ satellite image is consisted of multi-dimensional data structure and, there is target feature, which is purposed to extract and various background image is mixed. In this paper, in order to eliminate background features (tidal flat, seawater and etc) around target feature (laver farm) effectively, pixel spectrum sphere of target feature is projected onto the orthogonal spectrum sphere of background feature. The rest amount of spectrum sphere of target feature in the pixel can be presumed to remove spectrum sphere of background feature. In order to make sure the excellence of feature extraction method based on ICA, which is proposed in this paper, laver farm feature extraction from Landsat ETM+ satellite image is applied. Also, In the side of feature extraction accuracy and the noise level, which is still remaining not to remove after feature extraction, we have conducted a comparing test with traditionally most popular method, maximum-likelihood. As a consequence, the proposed method from this paper can effectively eliminate background features around mixed spectrum sphere to extract target feature. So, we found that it had excellent detection efficiency.

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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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    • 2002.10a
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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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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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    • 2007.07a
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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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Hybrid-Feature Extraction for the Facial Emotion Recognition

  • Byun, Kwang-Sub;Park, Chang-Hyun;Sim, Kwee-Bo;Jeong, In-Cheol;Ham, Ho-Sang
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.1281-1285
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    • 2004
  • There are numerous emotions in the human world. Human expresses and recognizes their emotion using various channels. The example is an eye, nose and mouse. Particularly, in the emotion recognition from facial expression they can perform the very flexible and robust emotion recognition because of utilization of various channels. Hybrid-feature extraction algorithm is based on this human process. It uses the geometrical feature extraction and the color distributed histogram. And then, through the independently parallel learning of the neural-network, input emotion is classified. Also, for the natural classification of the emotion, advancing two-dimensional emotion space is introduced and used in this paper. Advancing twodimensional emotion space performs a flexible and smooth classification of emotion.

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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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    • 2004.11c
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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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A GENETIC ALGORITHM BASED FEATURE EXTRACTION TECHNIQUE FOR HYPERSPECTRAL IMAGERY

  • Ryu Byong Tae;Kim Choon-Woo;Kim Hakil;Lee Kyu Sung
    • Proceedings of the KSRS Conference
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    • 2005.10a
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    • pp.209-212
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    • 2005
  • Hyperspectral data consists of more than 200 spectral bands that are highly correlated. In order to utilize hyperspectral data for classification, dimensional reduction or feature extraction is desired. By applying feature extraction, computational complexity of classification can be reduced and classification accuracy may be improved. In this paper, a genetic algorithm based feature extraction technique is proposed. Measure from discriminant analysis is utilized as optimization criterion. A subset of spectral bands is selected by genetic algorithm. Dimension of feature space is further reduced by linear transformation. Feasibility of the proposed technique is evaluated with AVIRIS data.

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