• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.64-79
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• 2017

Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.482-486
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• 2009

Scale-invariant feature is an effective method for retrieving and classifying images. In this study, we analyze a scale-invariant planar curve features for developing 2D shapes. Scale-space filtering is used to determine contour structures on different scales. However, it is difficult to track significant points on different scales. In mathematics, curvature is considered to be fundamental feature of a planar curve. However, the curvature of a digitized planar curve depends on a scale. Therefore, automatic scale detection for curvature analysis is required for practical use. We propose a technique for achieving automatic scale detection based on difference of curvature. Once the curvature values are normalized with regard to the scale, we can calculate difference in the curvature values for different scales. Further, an appropriate scale and its position are detected simultaneously, thereby avoiding tracking problem. Appropriate scales and their positions can be detected with high accuracy. An advantage of the proposed method is that the detected significant points do not need to be located in the same contour. The validity of the proposed method is confirmed by experimental results.

• 한국정보통신학회논문지
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• 제8권3호
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• pp.694-701
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• 2004

본 논문에서는 능동카메라 환경에서 카메라의 움직임에 의해 유발되는 광역움직임(global motion)과 이동물체에 의해 발생하는 지역움직임(local motion)을 분리한 후, 카메라 팬틸트를 제어하여 물체를 추적하는 특징기반의 추적 시스템을 제안했다. 제안한 시스템은 블록기반 움직임 계측을 통해 연속한 2 프레임 사이의 이동 움직임을 찾고, 이 움직임에서 카메라의 움직임으로 인한 광역 움직임을 제거함으로써 전경물체의 지역 움직임만을 추적한다. 이때, 배경만의 움직임만으로 카메라 움직임을 강건하게 계측하기 위하여, 블록기반 움직임에서 배경움직임을 분류하기 위한 지배적인 움직임 추출방법을 제시한다. 또한 분리된 지역움직임으로부터 잡음물체의 움직임을 제거하기 위하여 꼭지점 특징의 추적궤적 속성에 따른 군집화 알고리즘을 제안한다. 제안한 추적시스템은 여러가지 실험에서 좋은 결과를 보였다.

• Journal of information and communication convergence engineering
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• 제8권2호
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• pp.185-190
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• 2010

In this paper we propose the CBIR algorithm which is based on a novel image block method that combined both color and edge feature. The main drawback of global histogram representation is dependent of the color without spatial or shape information, a new image block method that divided the image to 8 related blocks which contained more information of the image is utilized to extract image feature. Based on these 8 blocks, histogram equalization and edge detection techniques are also used for image retrieval. The experimental results show that the proposed image block method has better ability of characterizing the image contents than traditional block method and can perform the retrieval system efficiently.

• ETRI Journal
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• 제34권4호
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• pp.572-582
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• 2012

The discrete Gaussian-Hermite moment (DGHM) is a global feature representation method that can be applied to square images. We propose a modified DGHM (MDGHM) method and an MDGHM-based scale-invariant feature transform (MDGHM-SIFT) descriptor. In the MDGHM, we devise a movable mask to represent the local features of a non-square image. The complete set of non-square image features are then represented by the summation of all MDGHMs. We also propose to apply an accumulated MDGHM using multi-order derivatives to obtain distinguishable feature information in the third stage of the SIFT. Finally, we calculate an MDGHM-based magnitude and an MDGHM-based orientation using the accumulated MDGHM. We carry out experiments using the proposed method with six kinds of deformations. The results show that the proposed method can be applied to non-square images without any image truncation and that it significantly outperforms the matching accuracy of other SIFT algorithms.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.487-490
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• 1999

In this paper we propose an efficient content-based image retrieval method using the color and wavelet based features. The color features are extracted from color histograms of the global image and the wavelet based features are extracted from the invariant moments of the high-pass band image through the spatial-frequency analysis of the wavelet transform. The proposed algorithm, called color and wavelet features based query(CWBQ), is composed of two-step query operations for efficient image retrieval: the coarse level filtering operation and the fine level matching operation. In the first filtering operation, the color histogram feature is used to filter out the dissimilar images quickly from a large image database. The second matching operation applies the wavelet based feature to the retained set of images to retrieve all relevant images successfully. The experimental results show that the proposed algorithm yields more improved retrieval accuracy with computationally efficiency than the previous methods.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2017년도 제55차 동계학술대회논문집 25권1호
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• pp.79-82
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• 2017

IoT devices are interconnected in global network with different functionalities and manage the data transfer in cloud computing. IoT devices can be used anytime, anywhere with any device with different applications and protocols. Same devices but different applications according to end user requirements such as sensors and Wi-Fi devices, reusability of these applications can enhance the development process. However, large number of variations in cloud computing make it difficult the features selection in application because of compatibility issues of devices. In this paper we have proposed multi-Software Product Lines (multi-SPLs) approach to manage the variabilities and commonalities of IoT applications and protocols. Feature modeling is used to manage the commonalities and variabilities of SPL. We proposed that multi-SPLs feature model is more appropriate for modeling of IoT applications and protocols.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 가을 학술발표논문집 Vol.31 No.2 (2)
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• pp.673-675
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• 2004

Local Feature Analysis(LFA)는 눈, 코, 턱 그리고 볼과 같은 얼굴의 지역적 특징을 잘 추출하는 것으로 알려져 있으나, 얼굴 인식에 이용하기에는 몇 가지 문제점이 있다. 본 논문에서는 LFA의 문제점을 개선하여 인식에 적합한 새로운 얼굴 특징 추출 방법을 제안한다. 제안 방법은 kernel 생성, 선택 그리고 중첩의 3 단계로 이루어진다. 첫 번째 단계에서 얼굴의 지역적 특징을 검출할 수 있는 kernel물 생성하고, 두 번째 단계에서 인식에 적합한 kernel을 선택한다. 마지막으로 선택된 kernel을 중첩시켜 적은 개수의 조밀한 형태의 kernel로 재 표현한다. 실험을 통하여 제안 방법이 적은 개수의 특징을 이용하여 좋은 인식율을 보임을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.19-21
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• 2004

In many cases, human's visual recognition depends on contextual information. We need to use effective feature information for performing vigorous place recognition to illumination, noise, etc. In the existing cases that use edge and color, etc., visual recognition doesn't cope effectively with real environment. To solve this problem, using natural marker, we improve the efficiency of place recognition.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
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• pp.683-686
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• 1988

In the recognition of similar patterns like profile contours of human faces, feature measure plays important role. We extracted effective and general feature by B-spline approximation. The nodes and vertices of the approximated curve are normalized and used as features. Since the features have both local property of curvature extrema and global property by B-spline approximation, they are superior to those of curvature extrema of the profile contour. For the image data of six sets of 56 persons, some of which are ill-made, averaged accuracy rate of 97.6 % is obtained in recognizing combinational 333 test samples.