• Title/Summary/Keyword: feature

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Parametric Design System Basedon Design Unit and Configuration Design Method (구성 설계방법과 설계유니트를 이용한 파라메트릭 설계 시스템)

  • 명세현;한순흥
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1995.10a
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    • pp.702-706
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    • 1995
  • Integration of CAM and CAM information is important in the CIM era. For a CIM system, the feature representation can be a solution to the integration of product model data. These are geometry feature, functional feature, and manufacturing feature in the feature context. This paper proposes a framework to integrate the configuration design method, parametric modeling and the feature modeling method. The concept of design unit which is one level higher than functional feature and parametric modeling concept with functional features have been proposed.

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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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Nonlinear Feature Transformation and Genetic Feature Selection: Improving System Security and Decreasing Computational Cost

  • Taghanaki, Saeid Asgari;Ansari, Mohammad Reza;Dehkordi, Behzad Zamani;Mousavi, Sayed Ali
    • ETRI Journal
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    • v.34 no.6
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    • pp.847-857
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    • 2012
  • Intrusion detection systems (IDSs) have an important effect on system defense and security. Recently, most IDS methods have used transformed features, selected features, or original features. Both feature transformation and feature selection have their advantages. Neighborhood component analysis feature transformation and genetic feature selection (NCAGAFS) is proposed in this research. NCAGAFS is based on soft computing and data mining and uses the advantages of both transformation and selection. This method transforms features via neighborhood component analysis and chooses the best features with a classifier based on a genetic feature selection method. This novel approach is verified using the KDD Cup99 dataset, demonstrating higher performances than other well-known methods under various classifiers have demonstrated.

Optimal feature extraction for normally distributed multicall data (가우시안 분포의 다중클래스 데이터에 대한 최적 피춰추출 방법)

  • 최의선;이철희
    • Proceedings of the IEEK Conference
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    • 1998.10a
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    • pp.1263-1266
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    • 1998
  • In this paper, we propose an optimal feature extraction method for normally distributed multiclass data. We search the whole feature space to find a set of features that give the smallest classification error for the Gaussian ML classifier. Initially, we start with an arbitrary feature vector. Assuming that the feature vector is used for classification, we compute the classification error. Then we move the feature vector slightly and compute the classification error with this vector. Finally we update the feature vector such that the classification error decreases most rapidly. This procedure is done by taking gradient. Alternatively, the initial vector can be those found by conventional feature extraction algorithms. We propose two search methods, sequential search and global search. Experiment results show that the proposed method compares favorably with the conventional feature extraction methods.

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The Optimal Bispectral Feature Vectors and the Fuzzy Classifier for 2D Shape Classification

  • Youngwoon Woo;Soowhan Han;Park, Choong-Shik
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2001.01a
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    • pp.421-427
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    • 2001
  • In this paper, a method for selection of the optimal feature vectors is proposed for the classification of closed 2D shapes using the bispectrum of a contour sequence. The bispectrum based on third order cumulants is applied to the contour sequences of the images to extract feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to represent two-dimensional planar images, but there is no certain criterion on the selection of the feature vectors for optimal classification of closed 2D images. In this paper, a new method for selecting the optimal bispectral feature vectors based on the variances of the feature vectors. The experimental results are presented using eight different shapes of aircraft images, the feature vectors of the bispectrum from five to fifteen and an weighted mean fuzzy classifier.

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A Study on the Performance Enhancement of Radar Target Classification Using the Two-Level Feature Vector Fusion Method

  • Kim, In-Ha;Choi, In-Sik;Chae, Dae-Young
    • Journal of electromagnetic engineering and science
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    • v.18 no.3
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    • pp.206-211
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    • 2018
  • In this paper, we proposed a two-level feature vector fusion technique to improve the performance of target classification. The proposed method combines feature vectors of the early-time region and late-time region in the first-level fusion. In the second-level fusion, we combine the monostatic and bistatic features obtained in the first level. The radar cross section (RCS) of the 3D full-scale model is obtained using the electromagnetic analysis tool FEKO, and then, the feature vector of the target is extracted from it. The feature vector based on the waveform structure is used as the feature vector of the early-time region, while the resonance frequency extracted using the evolutionary programming-based CLEAN algorithm is used as the feature vector of the late-time region. The study results show that the two-level fusion method is better than the one-level fusion method.

Feature Combination and Selection Using Genetic Algorithm for Character Recognition (유전 알고리즘을 이용한 특징 결합과 선택)

  • Lee Jin-Seon
    • The Journal of the Korea Contents Association
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    • v.5 no.5
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    • pp.152-158
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    • 2005
  • By using a combination of different feature sets extracted from input character patterns, we can improve the character recognition system performance. To reduce the dimensionality of the combined feature vector, we conduct the feature selection. This paper proposes a general framework for the feature combination and selection for character recognition problems. It also presents a specific design for the handwritten numeral recognition. Tn the design, DDD and AGD feature sets are extracted from handwritten numeral patterns, and a genetic algorithm is used for the feature selection. Experimental result showed a significant accuracy improvement by about 0.7% for the CENPARMI handwrittennumeral database.

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Construction of Composite Feature Vector Based on Discriminant Analysis for Face Recognition (얼굴인식을 위한 판별분석에 기반한 복합특징 벡터 구성 방법)

  • Choi, Sang-Il
    • Journal of Korea Multimedia Society
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    • v.18 no.7
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    • pp.834-842
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    • 2015
  • We propose a method to construct composite feature vector based on discriminant analysis for face recognition. For this, we first extract the holistic- and local-features from whole face images and local images, which consist of the discriminant pixels, by using a discriminant feature extraction method. In order to utilize both advantages of holistic- and local-features, we evaluate the amount of the discriminative information in each feature and then construct a composite feature vector with only the features that contain a large amount of discriminative information. The experimental results for the FERET, CMU-PIE and Yale B databases show that the proposed composite feature vector has improvement of face recognition performance.

An approach to analyze commonality and variability of feature based on Ontology in Software Product line Engineering (Software 제품계열공학에서 온톨로지에 기반한 feature의 공통성 및 가변성 분석모델)

  • Kim Jin-Woo;Lee Soon-Bok;Lee Tae-Woong;Baik Doo-Kwon
    • Proceedings of the Korean Information Science Society Conference
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    • 2006.06c
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    • pp.139-141
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    • 2006
  • 제품계열공학에서 feature diagram(FD)은 개발자의 직관이나 도메인 전문가의 경험에 근거하여 작성되어, feature간의 공통성 및 가변성분석 기준이 불명확하며 비정형적인 feature의 공통성 및 가변성 분석으로 인한 stakeholder의 공통된 이해가 부족한 문제점을 내포하고 있다. 따라서, 본 논문에서는 이를 해결하기 위하여 공통된 feature의 이해를 위해 feature 속성리스트에 기반한 메타 feature모델과 feature간의 의미유사성관계를 이용한 온톨로지를 적용한 공통성 및 가변성 분석모델을 제안한다.

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Size, Scale and Rotation Invariant Proposed Feature vectors for Trademark Recognition

  • Faisal zafa, Muhammad;Mohamad, Dzulkifli
    • Proceedings of the IEEK Conference
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    • 2002.07c
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    • pp.1420-1423
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    • 2002
  • The classification and recognition of two-dimensional trademark patterns independently of their position, orientation, size and scale by proposing two feature vectors has been discussed. The paper presents experimentation on two feature vectors showing size- invariance and scale-invariance respectively. Both feature vectors are equally invariant to rotation as well. The feature extraction is based on local as well as global statistics of the image. These feature vectors have appealing mathematical simplicity and are versatile. The results so far have shown the best performance of the developed system based on these unique sets of feature. The goal has been achieved by segmenting the image using connected-component (nearest neighbours) algorithm. Second part of this work considers the possibility of using back propagation neural networks (BPN) for the learning and matching tasks, by simply feeding the feature vectosr. The effectiveness of the proposed feature vectors is tested with various trademarks, not used in learning phase.

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