• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.522-524
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• 2012

For exact classification of the defect, good feature selection and classifier is necessary. In this paper, various features such as brightness features, shape features and statistical features are stated and Bayes classifier using Gaussian mixture model is used as classifier. Also feature extraction method based on spectral graph theory is presented. Experimental result shows that feature extraction method using graph Laplacian result in better performance than the result using PCA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.7
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• pp.1642-1653
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• 1996

This paper covers feature extraction for automatic handwritten signature verification. Several major feature selection techniques are investigated from a practical perspective to realise an optimal signature verification system, and customised feature set selection based on set-on-set distance measurement is presented. The experimental results have proved the proposed methods to be efficient, offering considerably improved verification performance compared to conventional methods. Also, they dramatically reduce the processing complexity in the verification system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.27-28
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• 2020

본 논문에서는 초음파 영상의 통계적 특징벡터를 활용하여 폐암 분류를 제안한다. 폐암의 초음파 사진들을 비교 분석하여 각각의 label에 맞는 feature vector를 선별한다. 선택된 feature vector는 SVM을 이용하여 훈련 시킨 후, 최종적으로 폐암을 구별한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.59-60
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• 2020

본 논문에서는 특징점 추출 알고리즘 중 SIFT(Scale Invariant Feature Transform)알고리즘을 사용하여 유의미한 특징점을 추출하기 위한 방법을 제안하고자한다. 추출된 특징점을 실제 이미지에 display 해봄으로써 성능을 확인해본다.

• Journal of information and communication convergence engineering
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• v.12 no.4
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• pp.263-270
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• 2014

Corner detection and feature extraction are essential aspects of computer vision problems such as object recognition and tracking. Feature detectors such as Scale Invariant Feature Transform (SIFT) yields high quality features but computationally intensive for use in real-time applications. The Features from Accelerated Segment Test (FAST) detector provides faster feature computation by extracting only corner information in recognising an object. In this paper we have analyzed the efficient object detection algorithms with respect to efficiency, quality and robustness by comparing characteristics of image detectors for corner detector and feature extractors. The simulated result shows that compared to conventional SIFT algorithm, the object recognition system based on the FAST corner detector yields increased speed and low performance degradation. The average time to find keypoints in SIFT method is about 0.116 seconds for extracting 2169 keypoints. Similarly the average time to find corner points was 0.651 seconds for detecting 1714 keypoints in FAST methods at threshold 30. Thus the FAST method detects corner points faster with better quality images for object recognition.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.129-136
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• 2015

We propose a hybrid face recognition to deal with illumination variation. For this, we extract discriminant features by using the different illumination invariant feature extraction methods. In order to utilize both advantages of each method, we evaluate the discriminant power of each feature by using the discriminant distance and then construct a composite feature with only the features that contain a large amount of discriminative information. The experimental results for the Multi-PIE, Yale B, AR and yale databases show that the proposed method outperforms an individual illumination invariant feature extraction method for all the databases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.9C
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• pp.876-883
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• 2003

In this paper, we propose an algorithm for organ recognition in ultrasound images using log power spectrum. The main procedure of the algorithm consists of feature extraction and feature classification. In the feature extraction, as a translation invariant feature, log power spectrum is used for extracting the information on echo of the organs tissue from a preprocessed input image. In the feature classification, Mahalanobis distance is used as a measure of the similarity between the feature of an input image and the representative feature of each class. Experimental results for real ultrasound images show that the proposed algorithm yields the improvement of maximum 30% recognition rate than the recognition algorithm using power spectrum and Euclidean distance, and results in better recognition rate of 10-40% than the recognition algorithm using weighted quefrency complex cepstrum.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.6
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• pp.650-662
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• 1989

In this paper, a new algorithm for feature extraction and classification of recognizing Hanguel patterns is proposed. Inputed patterns classify into six basic formal patterns and divided into subregion of Hanguel phoneme and extract the crook feature from position information of the each subregion. Hanguel patterns are defined and are made of the indexed-sequence file using these crook features points. Hanguel patterns are recognized by retrievignt ehses two files such as feature indexed-sequence file and standard dictionary file. Thi paper show that the algorithm is very simple and easily construct the software system. Experimental result presents the output of feature extraction and grouping of input patterns. Proposed algorithm extract the crooked feature using distance transformation method within the rectangle of enclosure the characters. That uses the informationof relative position feature. It represents the 97% of recognition ratio.

• Journal of Korean Society for Geospatial Information Science
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• v.6 no.1 s.11
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• pp.27-34
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• 1998

A Unique Feature Identifier(UFID) is a way of referring to a feature, generally representing a tangible feature in the real world. In other words, a UFID uniquely identifies the related feature in the database and is normally used to link two or more databases together. This paper presents a UFID system aiming at the internal uses for National Geography Institute(NGI) as well as external uses for National Geographic Information System(NGIS) generally to link datasets together. The advantage of the proposed type of UFID lies in the meaningful nature of the identifier in providing a direct spatial index - administrative area and feature code. The checksum character proposed in this research is designed to remove any uncertainty about the number being corrupt. It will account lot digit transposition during manual input as well as corruption in transfer or processing.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.322-326
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• 2004

This paper describes a new iris recognition method based on a shift-invariant wavelet sub-images. For the feature representation, we first preprocess an iris image for the compensation of the variation of the iris and for the easy implementation of the wavelet transform. Then, we decompose the preprocessed iris image into multiple subband images using a shift-invariant wavelet transform. For feature representation, we select a set of subband images, which have rich information for the classification of various iris patterns and robust to noises. In order to reduce the size of the feature vector, we quantize. each pixel of subband images using the Lloyd-Max quantization method Each feature element is represented by one of quantization levels, and a set of these feature element is the feature vector. When the quantization is very coarse, the quantized level does not have much information about the image pixel value. Therefore, we define a new similarity measure based on mutual information between two features. With this similarity measure, the size of the feature vector can be reduced without much degradation of performance. Experimentally, we show that the proposed method produced superb performance in iris recognition.